Previous IRI research seminars


285 Seminar 16-11-2017

Speaker: Ely Repiso ()
Title: On-line adaptive side-by-side human robot companion to approach a moving person to interact
Abstract:

In this paper, we present an on-line adaptive side-by-side human-robot companion to approach a moving person to interact with. Our framework makes the pair robot-human capable of overpass, in a joint way, the dynamic and static obstacles of the environment while they reach a moving goal, which is the person who wants to interact with the pair. We have defined a new moving final goal that depends on the environment, the movement of the group and the movement of the interacting person. Moreover, we modified the Extended Social Force model to include this new moving goal. The method has been validated over several situations in simulation.

Links: Speaker info

284 Seminar 9-11-2017

Speaker: Anaís Garrell ()
Title: Aerial Social Force Model: A new framework to accompany people using autonomous flying robots
Abstract:

We proposed a novel Aerial Social Force Model (ASFM) that allows autonomous flying robots to accompany humans in urban environments in a safe and comfortable manner. To date, we are not aware of other state-of-the-art method that accomplish this task. The proposed approach is a 3D version of the Social Force Model (SFM) for the field of aerial robots which includes an interactive human-robot navigation scheme capable of predicting human motions and intentions so as to safely accompany them to their final destination. ASFM also introduces a new metric to fine-tune the parameters of the force model, and to evaluate the performance of the aerial robot companion based on comfort and distance between the robot and humans. The presented approach is extensively validated in diverse simulations and real experiments, and compared against other similar works in the literature. ASFM attains remarkable results and shows that it is a valuable framework for social robotics applications, such as guiding people or human-robot interaction.

Links: Speaker info

283 Seminar 19-10-2017

Speaker: Alejandro Hernández ()
Title: 3D CNNs on Distance Matrices for Human Action Recognition
Abstract:

In this paper we are interested in recognizing human actions from sequences of 3D skeleton data. For this purpose we combine a 3D Convolutional Neural Network with body representations based on Euclidean Distance Matrices (EDMs), which have been recently shown to be very effective to capture the geometric structure of the human pose. One inherent limitation of the EDMs, however, is that they are defined up to a permutation of the skeleton joints, i.e., randomly shuffling the ordering of the joints yields many different representations. In oder to address this issue we introduce a novel architecture that simultaneously, and in an end-to-end manner,learns an optimal transformation of the joints, while optimizing the rest of parameters of the convolutional network. The proposed approach achieves state-of-the-art results on 3 benchmarks, including the recent NTU RGB-D dataset, for which we improve on previousLSTM-based methods by more than 10 percentage points, also surpassing other CNN-based methods while using almost 1000 times fewer parameters.

Links: Speaker info, ACM MM

282 Seminar 5-10-2017

Speaker: Victor Vaquero ()
Title: Deconvolutional Networks for Point-Cloud Vehicle Detection and Tracking in Driving Scenarios
Abstract:

Vehicle detection and tracking is a core ingredient for developing autonomous driving applications in urban scenarios. Recent image-based Deep Learning (DL) techniques are obtaining breakthrough results in these perceptive tasks. However, DL research has not yet advanced much towards processing 3D point clouds from lidar range-finders. These sensors are very common in autonomous vehicles since, despite not providing as semantically rich information as images, their performance is more robust under harsh weather conditions than vision sensors. In this paper we present a full vehicle detection and tracking system that works with 3D lidar information only. Our detection step uses a Convolutional Neural Network (CNN) that receives as input a featured representation of the 3D information provided by a Velodyne HDL-64 sensor and returns a per-point classification of whether it belongs to a vehicle or not. The classified point cloud is then geometrically processed to generate observations for a multi-object tracking system implemented via a number of Multi-Hypothesis Extended Kalman Filters (MH-EKF) that estimate the position and velocity of the surrounding vehicles. The system is thoroughly evaluated on the KITTI tracking dataset, and we show the performance boost provided by our CNN-based vehicle detector over a standard geometric approach. Our lidar-based approach uses about a 4% of the data needed for an image-based detector with similarly competitive results.

Links: Speaker info

281 Seminar 28-7-2017

Speaker: Ignasi Clavera ()
Title: Policy Transfer via Modularity and Rewad Guiding
Abstract:

Non-prehensile manipulation, such as pushing, is an important function for robots to move objects and is sometimes preferred as an alternative to grasping. However, due to unknown frictional forces, pushing has been proven a difficult task for robots. We explore the use of reinforcement learning to train a robot to robustly push an object. In order to deal with the sample complexity of training such a method, we train the pushing policy in simulation and then transfer this policy to the real world. In order to ease the transfer from simulation, we propose to use modularity to separate the learned policy from the raw inputs and outputs; rather than training end-to-end, we decompose our system into modules and train only a subset of these modules in simulation. We further demonstrate that we can incorporate prior knowledge about the task into the state space and the reward function to speed up convergence. Finally, we introduce reward guiding to modify the reward function and further reduce the training time. We demonstrate, in both simulation and real-world experiments, that such an approach can be used to reliably push an object from many initial positions and orientations.

Links: Speaker info

280 Seminar 27-7-2017

Speaker: Ricard Bordalba ()
Title: Randomized Kinodynamic Planning for Cable-suspended Parallel Robots
Abstract:

This paper proposes the use of a randomized kinodynamic planning technique to synthesize dynamic motions for cable-suspended parallel robots. Given two mechanical states of the robot, both with a prescribed position and velocity, the method attempts to connect them by a collision-free trajectory that respects the joint and force limits of the actuators, keeps the cables in tension, and takes the robot dynamics into account. The method is based on the construction of a bidirectional rapidly-exploring random tree over the state space. Remarkably, the technique can be used to cross forward singularities of the robot in a predictable manner, which extends the motion capabilities beyond those demonstrated in previous work. The paper describes experiments that show the performance of the method in point-to-point operations with specific cable-driven robots, but the overall strategy remains applicable to other mechanism designs.

Links: Speaker info, CableCon2017

279 Seminar 20-7-2017

Speaker: Anna Vera ()
Title: Robot assisted assessment and training: multimodal feedback approach to promote human sensorimotor learning
Abstract:

Rehabilitation and clinical studies focused almost exclusively on improving motor outcomes. However, in the last decade, there has been a growing interest in sensory feedback processing, in the contribution of sensory information for motor control and in its role in fostering neural plasticity through learning. In order to substitute or enhance sensory information, the use of external sensory feedback based on robotic device is promising. This approach results beneficial for motor outcomes, but it is not yet proved if such improvement is concurrent with sensory function modification and, in particular, whether such effect involves one of the most important sensory source for movement control, i. e. proprioception. This talk will provide the evidence for the beneficial effect of multimodal feedback-based robot assisted training on sensorimotor functions. In particular, I will propose a system of human-robot interaction where a mutual information is shared by using different feedback modalities, such as haptic, vibro-tactile and visual feedback, presenting related somatosensory and motor function changes and the consequent retention. The multimodal feedback approach enhances somatosensory and motor learning and may thus become an effective behavioral intervention for treating movement disorders.

Links: Speaker info

278 Seminar 11-7-2017

Speaker: Jingang Yi ()
Title: Robotics Research at RAM Lab of Rutgers University
Time: 10:00
Place: Meeting room floor -1
Abstract:

In this talk, I will review recent robotics research activities at the Robotics, Automation and Mechatronics (RAM) Lab at Rutgers University. These research activities and projects represent many current research trends in robotics and automation and mechatronics areas. In the first part of the talk, I will discuss our research in the autonomous robotics and navigation areas, such as human-inspired autonomous aggressive driving for active vehicle safety control, autonomous robotic technologies for non-destructive bridge deck inspection and rehabilitation, etc. In the second part of the talk, I will present our studies on understanding and control of physical human-robot interactions and its potential applications in healthcare and rehabilitation engineering for human postural disability patients. I will also report some other projects in robotics and automation areas at the RAM Lab at Rutgers.

Links: Speaker info

277 Seminar 19-5-2017

Speaker: Jérémie Deray ()
Title: Word Ordering and Document Adjacency for Large Loop Closure Detection in 2D Laser Maps
Abstract:

We address in this paper the problem of loop closure detection for laser-based simultaneous localization and mapping (SLAM) of very large areas. Consistent with the state of the art, the map is encoded as a graph of poses, and to cope with very large mapping capabilities, loop closures are asserted by comparing the features extracted from a query laser scan against a previously acquired corpus of scan features using a bag-ofwords (BoW) scheme. Two contributions are here presented. First, to benefit from the graph topology, feature frequency scores in the BoW are computed not only for each individual scan but also from neighboring scans in the SLAM graph. This has the effect of enforcing neighbor relational information during document matching. Secondly, a weak geometric check that takes into account feature ordering and occlusions is introduced that substantially improves loop closure detection performance. The two contributions are evaluated both separately and jointly on four common SLAM datasets, and are shown to improve the state-of-the-art performance both in terms of precision and recall in most of the cases. Moreover, our current implementation is designed to work at nearly frame rate, allowing loop closure query resolution at nearly 22 Hz for the best case scenario and 2 Hz for the worst case scenario.

Links: Speaker info, ICRA2017

276 Seminar 4-5-2017

Speaker: Lorenzo Porzi ()
Title: Learning Depth-aware Deep Representations for Robotic Perception
Abstract:

Exploiting RGB-D data by means of convolutional neural networks (CNNs) is at the core of a number of robotics applications, including object detection, scene semantic segmentation, and grasping. Most existing approaches, however, exploit RGB-D data by simply considering depth as an additional input channel for the network. In this paper we show that the performance of deep architectures can be boosted by introducing DaConv, a novel, general-purpose CNN block which exploits depth to learn scale-aware feature representations. We demonstrate the benefits of DaConv on a variety of robotics oriented tasks, involving affordance detection, object coordinate regression, and contour detection in RGB-D images. In each of these experiments we show the potential of the proposed block and how it can be readily integrated into existing CNN architectures.

Links: Speaker info, ICRA2017

275 Seminar 27-4-2017

Speaker: Albert Pumarola ()
Title: PL-SLAM: Real-Time Monocular Visual SLAM with Points and Lines
Abstract:

Low textured scenes are well known to be one of the main Achilles heels of geometric computer vision algorithms relying on point correspondences, and in particular for visual SLAM. Yet, there are many environments in which, despite being low textured, one can still reliably estimate line-based geometric primitives, for instance in city and indoor scenes, or in the so-called Manhattan worlds, where structured edges are predominant. In this paper we propose a solution to handle these situations. Specifically, we build upon ORB-SLAM, presumably the current state-of-the-art solution both in terms of accuracy as efficiency, and extend its formulation to simultaneously handle both point and line correspondences. We propose a solution that can even work when most of the points are vanished out from the input images, and, interestingly it can be initialized from solely the detection of line correspondences in three consecutive frames. We thoroughly evaluate our approach and the new initialization strategy on the TUM RGB-D benchmark and demonstrate that the use of lines does not only improve the performance of the original ORB-SLAM solution in poorly textured frames, but also systematically improves it in sequence frames combining points and lines, without compromising the efficiency.

Links: Speaker info, ICRA2017

274 Seminar 25-4-2017

Speaker: Martin Saska ()
Title: Cooperating groups of micro aerial vehicles: from theory and applications to our successful participation in the MBZIRC competition
Abstract:

Deployment of teams of Micro Aerial Vehicles (MAVs) in real world environments independently to precise motion capture systems (such as Vicon) is a subsequent step in current hectic research in the field of autonomous flying systems. The aim of this talk is to present latest results in our endeavor towards fully autonomous compact flocks of MAVs, which were achieved by the Multi-robot Systems group. Stabilization, control and motion planning techniques for steering swarms and formations of autonomous MAVs will be discussed with a focus on bio-inspired techniques that integrate swarming abilities of individual particles with a Model Predictive Control (MPC) methodology. Besides the basic principles of formation flying and swarm stabilization, examples of real-world applications of the introduced methods will be shown, including indoor documentation of large historical objects (churches) by formations of cooperating MAVs. Finally, a description of the system used in Mohamed Bin Zayed International Robotics Challenge (MBZIRC 2017), where our team won gold medals in the third challenge, silver medals in the first challenge and bronze medals in the GRAND-challenge, will be described together with our experience from the competition. MBZIRC is an international robotics competition organized every two years in Abu Dhabi by Sheikh Hamed Bin Zayed Al Nahyan with total prize-money and team sponsorship of USD 5 Million, which attracts the best robotic universities in the world (143 teams applied for participation in the contest). Our team participated in the Challenge 1, an autonomous landing of an MAV on a moving vehicle, and the Challenge 3, in which three cooperating MAVs search, locate, track, pick and place a set of static and moving objects.

Links: Speaker info

273 Seminar 20-4-2017

Speaker: Javier Romero ()
Title: Creating and exploiting data-driven human body models
Abstract:

Perception of people around us is a crucial part of everybody’s daylife. People’s analysis is not limited to parsing the current scene, including the posture and shape of humans in it. We can also imagine their appearance few seconds later, or how each individual would look like when performing a completely different motion or when gaining a few kilos. The main goal of my research is to provide these reasoning capabilities to a computer. To achieve this, we believe it is essential to have a computational model of human visual appearance. In this talk, I’ll give an overview of three challenges related to this topic that I have explored in recent years. The first one is to develop compact models of the geometry of human bodies from registered 3D scans, that can extrapolate to new poses, new shapes and new dynamic motions. Second, I will discuss how these models enable three different modalities of inference: fitting different data modalities in a generative manner, generating synthetic data for discriminative methods, and enabling weak supervision. Finally, the use of these models as an introspection tool for studying our own human perception of bodies will be described.

Links: Speaker info

272 Seminar 30-3-2017

Speaker: Angel Santamaria ()
Title: Trajectory Generation for Unmanned Aerial Manipulators through Quadratic Programming
Abstract:

In this paper a trajectory generation approach using quadratic programming is described for aerial manipulation, i.e. for the control of an aerial vehicle equipped with a robot arm. The proposed approach applies the online active set strategy to generate a feasible trajectory of the joints, in order to accomplish a set of tasks with defined bounds and constraint inequalities. The definition of the problem in the acceleration domain allows to integrate and perform a large set of tasks and, as a result, to obtain smooth motion of the joints. A weighting strategy, associated with a normalization procedure, allows to easily define the relative importance of the tasks. This approach is useful to accomplish different phases of a mission with different redundancy resolution strategies. The performance of the proposed technique is demonstrated through real experiments with all the algorithms running onboard in real time. In particular, the aerial manipulator can successfully perform navigation and interaction phases, while keeping motion within prescribed bounds and avoiding collisions with external obstacles.

Links: Speaker info, ICRA2017

271 Seminar 13-2-2017

Speaker: Jordi Pont-Tuset ()
Title: One-Shot Video Object Segmentation
Abstract:

In this talk I’ll present our recent work on semi-supervised video object segmentation, i.e., the separation of an object from the background in a video, given the mask of the first frame. We present One-Shot Video Object Segmentation (OSVOS), based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground segmentation, and finally to learning the appearance of a single annotated object of the test sequence (hence one-shot). Although all frames are processed independently, the results are temporally coherent and stable. We perform experiments on three annotated video segmentation databases, which show that OSVOS is fast and improves the state of the art by a significant margin (79.8% vs 68.0%).

Links: Speaker info

270 Seminar 26-1-2017

Speaker: Beta-Robots ()
Title: Boosting robotics research towards the market
Abstract:

Beta Robots is a start-up company that provides expertise and project execution on robotics and artificial perception systems. The company was founded by three former IRI members who finished their PhD degrees some years ago, in the domains of mobile robots navigation, grasp planning and kinematics. Beta Robots main purpose is to boost state of the art research results to operational prototypes close to the market needs and constraints, especially, but not limited to, the field of robotics. This provides the customers with resources to move forward their technological aspirations. The talk will overview Beta Robots’ current projects and will share the company vision, as well as discuss personal insights about the hot start-up environment in the robotics field.

Links: Speaker info

269 Seminar 12-1-2017

Speaker: Jan Funke ()
Title: The CREMI Challenge -- Neural Circuit Reconstruction from Electron Microscopy Images
Abstract:

The reconstruction of neural circuits from model organisms like Drosophila melanogaster is a key step for gaining new insights into the function of nervous systems. Currently, only electron microscopy provides the resolution to resolve synaptic connections. Given the huge amounts of data acquired for even small animals, computer vision methods are needed to segment neurons and find their synaptic connections. Despite successful efforts for the segmentation of neurons, the automatic identification of synaptic partners--and thus neural circuits--has largely been ignored, so far. Here, we present the first results of a challenge that we organize to foster contributions of the computer vision community to reconstruct neural circuits. The challenge provides six large volumes of electron microscopy data, with complete annotations for neurons, synapses, and synaptic partners. We present contributions made for each category and introduce the best approaches, which were also awarded at the MICCAI 2016 conference. Furthermore, we discuss the future of the challenge and the potential impact on neuroscientific research.

Links: Speaker info

268 Seminar 1-12-2016

Speaker: Brais Cancela ()
Title: A dynamic scene modeling approach to model human behavior in crowded scenes
Abstract:

Human behavior analysis is one of the most active computer vision research fields. As the number of cameras are increased, especially in restricted environments, like airports, train stations or museums, the need of automatic systems that can catalog the information provided by the cameras becomes crucial. In the case of crowded scenes, it is very difficult to distinguish people behavior because of the lack of visual contact of the whole body. Thus, behavior analysis remains in the evaluation of trajectories, adding high-level knowledge approaches in order to use that information in several applications like video surveillance or traffic analysis. A novel behavior analysis system, which includes information about the environment, is provided. It is based in the idea that every person tries to reach a goal in the scene following the same path the majority of people should use.

Links: Speaker info

267 Seminar 3-11-2016

Speaker: Jesús Bermúdez ()
Title: Minimal solutions for line fitting in omnidirectional vision
Abstract:

The original purpose of omnidirectional cameras is to increase the field of view (FOV) of cameras. In particular when considering line features the purpose is increasing the visibility of the projected segments. However due to nature of line projection it is also possible to estimate the calibration of the system and in the case of non-central cameras, reconstructing 3D lines from a single view. This talk is focused on the geometry of line projections (line-images) in omnidirectional systems. More in detail we present a set of minimal solutions for dealing with line-features extraction, calibration and 3D reconstruction.

Links: Speaker info

266 Seminar 20-10-2016

Speaker: Edgar Simo ()
Title: Fusing Global and Local Image Priors for Automatic Image Colorization
Abstract:

We present a novel technique to automatically colorize grayscale images that combines both global priors and local image features. Based on Convolutional Neural Networks, our deep network features a fusion layer that allows us to elegantly merge local information dependent on small image patches with global priors computed using the entire image. The entire framework, including the global and local priors as well as the colorization model, is trained in an end-to-end fashion. Furthermore, our architecture can process images of any resolution, unlike most existing approaches based on CNN. We leverage an existing large-scale scene classification database to train our model, exploiting the class labels of the dataset to more efficiently and discriminatively learn the global priors. We validate our approach with a user study and compare against the state of the art, where we show significant improvements. Furthermore, we demonstrate our method extensively on many different types of images, including black-and-white photography from over a hundred years ago, and show realistic colorizations.

Links: Speaker info

265 Seminar 4-7-2016

Speaker: Xavier Alameda-Pineda ()
Title: Matrix Completion: A vision-oriented perspective
Abstract:

Matrix completion is a generic framework aiming to recover a matrix from a limited number of (possibly noisy) entries. In this content, low-rank regularizers are often imposed so as to find matrix estimators that are robust to noise and outliers. In this talk I will discuss three recent advances on matrix completion, developed to solve three different vision applications. First, coupled matrix completion to solve joint head and body pose estimation. Second, non-linear matrix completion to recognize emotions from abstract paintings. Third, self-adaptive matrix completion for remote heart-rate estimation from videos.

Links: Speaker info

264 Seminar 30-6-2016

Speaker: Antonio Agudo ()
Title: Reconstructing a dynamic world
Abstract:

The simultaneous 3D reconstruction of rigid structures and the camera motion using a set of uncalibrated images has been extensively studied over the last few decades. The rigidity prior has proven to be a powerful constraint to solve the problem, allowing practical and robust solutions. However, rigid reconstruction techniques fail when applied directly to time-deforming objects such as a piece of cloth or a beating heart. In this talk, I will discuss the non-rigid case and present some results on real video sequences.

Links: Speaker info

263 Seminar 16-6-2016

Speaker: Edmundo Guerra ()
Title: Data association and validation for probabilistic localization and mapping
Abstract:

The development of fully autonomous robots still presents several challenges. One of these problems is the perception and interpretation of previously unknown environments and spatial relationships between landmarks, known simultaneous localization and mapping problem. The SLAM problem has typically been addressed from a probabilistic approach, with its roots on filtering techniques. To measure landmarks and proceed with the iterative improvement of the map, said landmarks need to be detected and identified on subsequent measurements, presenting the data association problem. This problem is has many solutions dependent on specific type of sensors, though most of them rely on the implementation of a validation gate in order to detect/prune incorrect associations that can disrupt the map. In this presentation, the most common data association techniques used for filter-based SLAM are reviewed, and results obtained with a derivate of the Joint Compatibility Branch and Bound technique are presented.

Links: Speaker info

262 Seminar 9-6-2016

Speaker: David Martínez ()
Title: Learning Relational Dynamics of Stochastic Domains for Planning
Abstract:

Probabilistic planners are very flexible tools that can provide good solutions for difficult tasks. However, they rely on a model of the domain, which may be costly to either hand code or automatically learn for complex tasks. We propose a new learning approach that (a) requires only a set of state transitions to learn the model; (b) can cope with uncertainty in the effects; (c) uses a relational representation to generalize over different objects; and (d) in addition to action effects, it can also learn exogenous effects that are not related to any action, e.g., moving objects, endogenous growth and natural development. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. Finally, experimental validation is provided that shows improvements over previous work.

Links: Speaker info, ICAPS-16

261 Seminar 2-6-2016

Speaker: Germán Ros ()
Title: Semantic Segmentation for Driving Scenarios: On virtual worlds and embedded platforms
Abstract:

This talk addresses some of the current challenges of semantic segmentation of driving environments, with focus on how to exploit virtual worlds to boost the accuracy and generalization capabilities of new models while we try to constraints these models to fit in embedded systems. When one aims for developing systems capable of running in real driving environments there are critical questions to solve, ranging from the size of the models (that need to be loaded on a chip with severe memory constraints) to the accuracy of the model for new unseen domains (i.e., new cities). Is it possible to have a compact model with state-of-the-art accuracy but small enough to operate on a chip? Are state-of-the-art approaches good enough to be used in autonomous driving? What are the current limitations of these systems? Are virtual worlds really useful? These are some of the questions addressed by this talk.

Links: Speaker info

260 Seminar 12-5-2016

Speaker: Farzad Husain ()
Title: Combining Semantic and Geometric Features for Object Class Segmentation of Indoor Scenes
Abstract:

Scene understanding is a necessary prerequisite for robots acting autonomously in complex environments. Low-cost RGB-D cameras such as Microsoft Kinect enabled new methods for analyzing indoor scenes and are now ubiquitously used in indoor robotics. We investigate strategies for efficient pixelwise object class labeling of indoor scenes that combine both pretrained semantic features transferred from a large color image dataset and geometric features, computed relative to the room structures, including a novel distance-from-wall feature, which encodes the proximity of scene points to a detected major wall of the room. We evaluate our approach on the popular NYU v2 dataset. Several deep learning models are tested, which are designed to exploit different characteristics of the data. This includes feature learning with two different pooling sizes. Our results indicate that combining semantic and geometric features yields significantly improved results for the task of object class segmentation.

Links: Speaker info, ICRA-16


Speaker: Farzad Husain ()
Title: Action Recognition based on Efficient Deep Feature Learning in the Spatio-Temporal Domain
Abstract:

Hand-crafted feature functions are usually designed based on the domain knowledge of a presumably controlled environment and often fail to generalize, as the statistics of real-world data cannot always be modeled correctly. Data-driven feature learning methods, on the other hand, have emerged as an alternative that often generalize better in uncontrolled environments. We present a simple, yet robust, 2D convolutional neural network extended to a concatenated 3D network that learns to extract features from the spatio-temporal domain of raw video data. The resulting network model is used for content-based recognition of videos. Relying on a 2D convolutional neural network allows us to exploit a pretrained network as a descriptor that yielded the best results on the largest and challenging ILSVRC-2014 dataset. Experimental results on commonly used benchmarking video datasets demonstrate that our results are state-of-the-art in terms of accuracy and computational time without requiring any preprocessing (e.g., optic flow) or a priori knowledge on data capture (e.g., camera motion estimation), which makes it more general and flexible than other approaches. Our implementation is made available.

Links: Speaker info, ICRA-16

Seminar 5-5-2016

259 Speaker: Andreu Corominas ()
Title: Observability Analysis and Optimal Sensor Placement in Stereo Radar Odometry
Abstract:

Localization is the key perceptual process closing the loop of autonomous navigation, allowing self-driving vehicles to operate in a deliberate way. To ensure robust localization, autonomous vehicles have to implement redundant estimation processes, ideally independent in terms of the underlying physics behind sensing principles. This paper presents a stereo radar odometry system, which can be used as such a redundant system, complementary to other odometry estimation processes, providing robustness for long-term operability. The presented work is novel with respect to previously published methods in that it contains: (i) a detailed formulation of the Doppler error and its associated uncertainty; (ii) an observability analysis that gives the minimal conditions to infer a 2D twist from radar readings; and (iii) a numerical analysis for optimal vehicle sensor placement. Experimental results are also detailed that validate the theoretical insights.

Links: Speaker info, ICRA-16


258 Speaker: Patrick Grosch ()
Title: Geometric Path Planning without Maneuvers for Non-Holonomic Parallel Orienting Robots
Abstract:

Current geometric path planners for nonholonomic parallel orienting robots generate maneuvers consisting of a sequence of moves connected by zero-velocity points. The need for these maneuvers restrains the use of this kind of parallel robots to few applications. Based on a rather old result on linear time-varying systems, this paper shows that there are infinitely differentiable paths connecting two arbitrary configurations in SO(3) such that the instantaneous axis of rotation along the path rest on a fixed plane. This theoretical result leads to a practical path planner for non-holonomic parallel orienting robots that generates single-move maneuvers. To present this result, we start with a path planner based on three-move maneuvers, and then we proceed by progressively reducing the number of moves to one, thus providing a unified treatment with respect to previous geometric path planners.

Links: Speaker info, ICRA-16

257 Seminar 13-4-2016

Speaker: Aleix Martínez ()
Title: My Adventured with Bayes: In search of optimal solutions in machine learning, computer vision and beyond
Abstract:

The Bayes criterion is generally regarded as the holy grail in classification because, for known distributions, it leads to the smallest possible classification error. Unfortunately, the Bayes classification boundary is generally nonlinear and its associated error can only be calculated under unrealistic assumptions. In this talk, we will show how these obstacles can be readily and efficiently averted yielding Bayes optimal algorithms in machine learning, statistics, computer vision and other areas of scientific inquiry. In this journey, we will extend the notion of homoscedasticity (meaning of the same variance) to spherical-homoscedasticity (meaning of the same variance up to a rotation) and show how this allows us to generalize the Bayes criterion under more realistic assumptions. This will lead to a new concept of kernel mappings with applications in classification (machine learning), shape analysis (statistics), structure from motion (computer vision), and others. We will then define other optimization criteria where Bayes cannot be readily applied and define the use of kernels in labeled graphs.

Links: Speaker info

256 Seminar 7-4-2016

Speaker: Adrià Colomé ()
Title: Using bad data for Reinforcement Learning with Policy Search algorithms to obtain better convergence to solutions
Abstract:

In the field of Policy Search, a type Reinforcement Learning, the usual approach is to obtain new policies from experimentation, where only the good data samples with high reward have a signifficant effect on the policy update. In this presentation, we will briefly do an overview of one of the most popular policy search algorithms: Relative Entropy Policy Search, and a new generalization of it putting emphasis on using bad data to improve the solutions obtained.

Links: Speaker info

Seminar 10-3-2016

Speaker: Joan Solà ()
Title: WOLF: a versatile framework for localization and mapping
Abstract:

The problem of robot localization in unknown environments is central in mobile robotics. It is essential for undertaking almost any kind of task requiring motion. Enriching it with mapping capabilities is almost as important: the robot can then plan its movements according to the environment, either making use of it, or avoiding the objects in it. In robotics, such problems fall into two major categories: Odometry, where one integrates motions to obtain an estimate of the current position, and SLAM (simultaneous localization and mapping), where one builds a map of the environment, useful both for relocalizing and motion planning. Odometry and SLAM systems share a great deal of common pieces.

The effort required to build such systems is very often enormous, and encompasses the mastering of a great deal of complex techniques, that range from sensor data processing (e.g. computer vision), to advanced estimation solvers for huge non-linear problems (e.g. incremental Cholesky factorization).

Our goal is to provide a proper framework for building good localization systems, by building on top of great work made by other contributors, therefore allowing the engineering of localization solutions for a vast diversity of projects, both at IRI and elsewhere.

Together with some collaborators at LAAS-CNRS, we are preparing at IRI a general framework, called WOLF, for solving suck kind of problems. Wolf is general mainly in three senses:

* One can configure a robotic system with virtually any number of sensors, of any kind, and have them either synchronized or not. One may be interested, also, in auto-calibrating the set of sensors, especially with respect to their extrinsic parameters (to speed up sensor installation), but also the intrinsic ones.

* One can use different engines for solving the problem, from the classical EKF to the advanced incremental solvers using non-linear optimization.

* One can be doing odometry or SLAM.

The basic Wolf structure is a tree of base classes reproducing the elements of the robotic problem. This is called the Wolf Tree. It has a robot, with sensors, with a trajectory formed by keyframes, and the map with its landmarks. Each sensor is equipped with processors allowing automatic processing of the received raw data. These base classes can be derived to build the particularizations you want: Wolf provides the basic functionality in the base classes, and you add what you want on top.

Wolf can be used within ROS for an easy integration. We provide examples of ROS nodes using Wolf. Wolf can also be used in other robotics frameworks.

Links: Speaker info

254 Seminar 15-2-2016

Speaker: Javier Civera ()
Title: Mid and high-level features for dense monocular SLAM
Abstract:

The traditional focus of visual SLAM has been the geometric model of point-based features; and only recently a more semantic understanding and the modelling of scene priors has gained relevance in the community. Learning and understanding patterns beyond a local geometric/photometric scope opens the door to a wider array of applications and improves the performance of the traditional methods. In this talk I will review some of the most relevant papers and my own work in this direction, highlighting the improvements and current limitations of mid-level and high-level scene features in the visual SLAM domain.

Links: Speaker info

253 Seminar 17-12-2015

Speaker: Francesc Serratosa ()
Title: Machine Learning, Human Interaction and Graph Matching applied to Mobile Robots
Abstract:

In this seminar, we show how we have put together machine learning and graph matching to deduct the position of mobile robots in a semi-automatic way and considering the input of robots is only their embedded cameras. First, we are going to explain how machine-learning techniques have been applied to graph matching to increase the quality of the final node-to-node correspondence and also, how human interactivity has been applied in the process of matching two attributed graphs. Then, we are going to present a system that is able to deduct the position of some cameras given their images and also some human interaction. This system is based on well-known feature extraction and structure from motion methods. Moreover, we have used the previously commented learning techniques, cooperative pose estimation and human interaction.

Links: Speaker info

252 Seminar 26-11-2015

Speaker: Sergi Foix ()
Title: 3D Sensor planning framework for leaf probing
Abstract:

Modern plant phenotyping requires active sensing technologies and particular exploration strategies. This article proposes a new method for actively exploring a 3D region of space with the aim of localizing special areas of interest for manipulation tasks over plants. In our method, exploration is guided by a multi-layer occupancy grid map. This map, together with a multiple-view estimator and a maximum information-gain gathering approach, incrementally provides a better understanding of the scene until a task termination criterion is reached.
This approach is designed to be applicable for any task entailing 3D object exploration where some previous knowledge of its general shape is available. Its suitability is demonstrated here for an eye-in-hand arm configuration in a leaf probing application.

Links: Speaker info, IROS-15

251 Seminar 16-11-2015

Speaker: Victor Vaquero ()
Title: Real Time People Detection Combining Appearance and Depth Image Spaces using Boosted Random Ferns
Abstract:

This paper presents a robust and real-time method for people detection in urban and crowed environments. Unlike other conventional methods which either focus on single features or compute multiple and independent classifiers specialized in a particular feature space, the pro- posed approach creates a synergic combination of appearance and depth cues in a unique classifier. The core of our method is a Boosted Random Ferns classifier that selects automatically the most discriminative local binary features for both the appearance and depth image spaces. Based on this classifier, a fast and robust people detector which maintains high detection rates in spite of environmental changes is created. The proposed method has been validated in a challenging RGB-D database of people in urban scenarios and has shown that outperforms state-of- the-art approaches in spite of the difficult environment conditions. As a result, this method is of special interest for real-time robotic applications where people detection is a key matter, such as human-robot interaction or safe navigation of mobile robots for example..

Links: Speaker info, ROBOT-15

Speaker: Victor Vaquero ()
Title: Low Cost, Robust and Real Time System for Detecting and Tracking Moving Objects to Automate Cargo Handling in Port Terminals
Abstract:

The presented paper addresses the problem of detecting and tracking moving objects for autonomous cargo handling in port terminals using a perception system which input data is a single layer laser scanner. A computationally low cost and robust Detection and Tracking Moving Objects (DATMO) algorithm is presented to be used in autonomous guided vehicles and autonomous trucks for efficient transportation of cargo in ports. The method first detects moving objects and then tracks them, taking into account that in port terminals the structure of the environment is formed by containers and that the moving objects can be trucks, AGV, cars, straddle carriers and people among others. Two approaches of the DATMO system have been tested, the first one is oriented to detect moving obstacles and focused on tracking and filtering those detections; and the second one is focused on keepking targets when no detections are provided. The system has been evaluated with real data obtained in the CTT port terminal in Hengelo, the Netherlands. Both methods have been tested in the dataset with good results in tracking moving objects.

Links: Speaker info, ROBOT-15

250 Seminar 12-11-2015

Speaker: Angel Santamaria ()
Title: High-Frequency MAV State Estimation Using Low-Cost Inertial and Optical Flow Measurement Units
Abstract:

This paper develops a simple and low-cost method for 3D, high-rate vehicle state estimation, specially designed for free-flying Micro Aerial Vehicles (MAVs). We fuse observations from inertial measurement units and the recently appeared low-cost optical flow smart cameras. These smart cameras integrate a sonar altimeter, a triaxial gyrometer and an optical flow sensor, and directly provide metric ego-motion information in the form of body velocities and altitude. Compared to state-of-the-art visual-inertial odometry methods, we are able to drastically reduce the computational load in the main processor unit, and obtain an accurate estimation of the vehicle state at a high update rate of 100Hz. We thus extend the current use of these smart cameras from hovering purposes to odometry estimation. In order to propose a simple algorithmic solution, we investigate the performances of two Kalman filters, in the extended and error-state flavors, alongside a large number of algorithm variations, using simulations and real experiments with precise ground-truth. We observe that the marginal performance gain attained with these algorithm improvements does not pay for the effort of implementing them. We conclude that a classical EKF in its simplest form is sufficient for providing motion estimates that coherently exploit the available measurements.

Links: Speaker info, IROS-15

249 Seminar 15-10-2015

Speaker: David Martinez ()
Title: Safe Robot Execution in Model-Based Reinforcement Learning
Abstract:

Task learning in robotics requires repeatedly executing the same actions in different states to learn the model of the task. However, in real-world domains, there are usually sequences of actions that, if executed, may produce unrecoverable errors (e.g. breaking an object). Robots should avoid repeating such errors when learning, and thus explore the state space in a more intelligent way. This requires identifying dangerous action effects to avoid including such actions in the generated plans, while at the same time enforcing that the learned models are complete enough for the planner not to fall into dead-ends.
We thus propose a new learning method that allows a robot to reason about dead-ends and their causes. Some such causes may be dangerous action effects (i.e., leading to unrecoverable errors if the action were executed in the given state) so that the method allows the robot to skip the exploration of risky actions and guarantees the safety of planned actions. If a plan might lead to a dead-end (e.g., one that includes a dangerous action effect), the robot tries to find an alternative safe plan and, if not found, it actively asks a teacher whether the risky action should be executed.
This method permits learning safe policies as well as minimizing unrecoverable errors during the learning process. Experimental validation of the approach is provided in two different scenarios: a robotic task and a simulated problem from the international planning competition. Our approach greatly increases success ratios in problems where previous approaches had high probabilities of failing.

Links: Speaker info, IROS-15

248 Seminar 17-7-2015 (at the FME conference room)

Speaker: Antonio Torralba ()
Title: Large Scale Visual Scene Recognition
Abstract:

Human visual scene understanding is remarkable: with only a brief glance at an image, an abundance of information is available - spatial structure, scene category and the identity of main objects in the scene. In this talk I will describe some of our work on scene and object recognition, and in building models of human vision (including eye movements and image memory).

Links: Speaker info

247 Seminar 16-7-2015

Speaker: Oscar Martinez Mozos ()
Title: Monitoring Technologies to Support Robotic Assistants
Abstract:

During the first minutes of this talk I will give a brief introduction about my past research on robotic perception. Afterwards, my current work on different technologies to monitor the activity of people and their mental state. In particular, I will concentrate on stress monitoring, and activities for daily life (ADL) monitoring. The final goal is to use these technologies to support a service robot that will assists people during their everyday activities, with the final aim of improving their quality of life.

Links: Speaker info

246 Seminar 6-7-2015 (at 10:00 at FME meeting room)

Speaker: Vincent Lepetit ()
Title: Hands Deep in Deep Learning for Hand Pose Estimation
Abstract:

We introduce and evaluate several architectures for Convolutional Neural Networks to predict the 3D joint locations of a hand given a depth map. We first show that a prior on the 3D pose can be easily introduced and significantly improves the accuracy and reliability of the predictions. We also show how to use context efficiently to deal with ambiguities between fingers. These two contributions allow us to significantly outperform the state-of-the-art on several challenging benchmarks, both in terms of accuracy and computation times. A recent arXiv paper independently shows that our approach outperforms the other ones on the existing benchmarks.

Links: Speaker info

245 Seminar 11-6-2015

Speaker: Ricardo Tellez ()
Title: The Construct: robotics simulations in the cloud
Abstract:

Robotics tools are the key to good research work. One of those basic tools is the simulator. Current robotics simulators they are very expensive, or they lack a lot of simulation features or they are slow to be run.
The Construct comes as a solution to all this problems. It is a web page from where to execute any robotic simulator in the market. The user can choose among a list of simulators to run. Simulators are executed in the cloud, freeing the user computer from high load. Simulations can be executed using any device or machine with a WebGL browser (Linux, Windows, Mac, iOS, Android...). Old versions of simulations are forever available. You can share your simulation and work at the same time with your colleagues. And many more features.

Links: Speaker info, The Construct Sim

244 Seminar 27-5-2015 (Wednesday)

Speaker: Federico Thomas ()
Title: Approaching Dual Quaternions from Matrix Algebra
Abstract:

Dual quaternions give a neat and succinct way to encapsulate both translations and rotations into a unified representation that can easily be concatenated and interpolated. Unfortunately, the combination of quaternions and dual numbers seem quite abstract and somewhat arbitrary when approached for the first time. Actually, the use of quaternions or dual numbers separately are already seen as a break in mainstream robot kinematics, which is based on homogeneous transformations. In this talk we will see how dual quaternions arise in a natural way when approximating 3D homogeneous transformations by 4D rotation matrices. This results in a seamless presentation of rigid-body transformations based on matrices and dual quaternions which permits building intuition about the use of quaternions and their generalizations.

Links: Speaker info

Seminar 14-5-2015

243 Speaker: Michael Villamizar ()
Title: Modeling Robot's World with Minimal Effort
Abstract:

We propose an efficient human-to-robot interaction approach to efficiently model the appearance of all relevant objects in robot's environment. Given an input video stream recorded while the robot is navigating, the user just needs to annotate a very small number of frames to build specific classifiers for each of the objects of interest. At the core of the method lie several random ferns classifiers, that share the same features and are updated online. The resulting methodology is fast (runs at 8 fps), versatile (it can be applied to unconstrained scenarios), scalable (real experiments show we can model up to 30 different object classes), and minimizes the amount of human intervention by leveraging the uncertainty measures associated to each classifier. We thoroughly validate the approach on synthetic data and on real sequences acquired with a mobile platform in outdoor and challenging scenarios containing a multitude of different objects. We show that the human can, with minimal effort, provide the robot with a detailed model of the objects in the scene.

Links: Speaker info



242 Speaker: Michael Villamizar ()
Title: Efficient Monocular Pose Estimation for Complex 3D Models
Abstract:

We propose a robust and efficient method to estimate the pose of a camera with respect to complex 3D textured models of the environment that can potentially contain more than 100; 000 points. To tackle this problem we follow a top down approach where we combine high-level deep network classifiers with low level geometric approaches to come up with a solution that is fast, robust and accurate. Given an input image, we initially use a pre-trained deep network to compute a rough estimation of the camera pose. This initial estimate, constrains the number of 3D model points that can be seen from the camera viewpoint. We then establish 3D-to-2D correspondences between these potentially visible points of the model and the 2D detected image features. Accurate pose estimation is finally obtained from the 2D-to-3D correspondences using a novel PnP algorithm that rejects outliers without the need to use a RANSAC strategy, and which is between 10 and 100 times faster than other methods that use it. Two real experiments dealing with very large and complex 3D models demonstrate the effectiveness of the approach.

Links: Speaker info

241 Seminar 8-5-2015

Speaker: Gerhard Neumann ()
Title: Learning Modular Control Policies in Robotics
Abstract:

One big aim in robotics is to learn modular control policies that can synthesize complex behaviour out of simpler elemental movements, often called movement primitives. Such structure of the control policy comes with the promise of simplifying complex learning problems into simpler tasks and alleviates learning of new, but similar tasks. In order to learn modular control policies efficiently, the underlying learning algorithm as well as the movement primitive representation have to fulfil several requirements. We need simple mechanisms to adapt the primitive to new situations and we need to learn how to sequence primitives and combine primitives simultaneously such that we can synthesize complex behaviour out of a compact set of movement primitives.

In this talk I will introduce our recent work on learning such a modular control policy with information theoretic policy search. Information-theoretic policy search uses an information-theoretic bound to determine the step-size of the policy update. It exhibits several beneficial properties, such as a smooth and stable learning process and a fast learning speed. We extended information-theoretic policy search methods such that we can efficiently generalize elemental movements to new situations, learn to select between several elemental movements and learn how to sequence elemental movements. Furthermore, I will present a new probabilistic movement primitive (ProMP) representation that is particularly well suited for such a modular control approach. ProMPs allow us to use new probabilistic operators that provide a principled way of generalization and co-activation of movement primitives.

Links: Speaker info

240 Seminar 27-4-2015 (at 11:00)

Speaker: Jan Funke ()
Title: Neuron Reconstruction from Anisotropic Electron Microscopy Volumes
Abstract:

In this talk, I will give an overview about our current state of the art in neuron reconstruction, i.e., the problem of the automatic extraction of the wiring diagram of a nervous system from electron microscopy volumes. Our methods focus on anisotropic volumes with high x- and y-resolution but low z-resolution, as obtained by serial section electron microscopy imaging procedures. Due to this anisotropy, we treat the data as a stack of 2D images, rather then a continuous 3D volume. Consequently, in order to reconstruct the 3D shape of each neuron, our model solves a multi-object tracking problem, where the objects are neuron slices, i.e., 2D cross-sections of neurons. For that, we consider multiple slice candidates in each image, which we link with possible assignment across images. We show how a globally cost-minimal segmentation of neuron slices and assignments between images can be found jointly and efficiently. Finally, I show how random sampling in a CRF can be used for candidate generation, and how the structured learning framework helps to find optimal parameters for our model.

Links: Speaker info

Seminar 23-4-2015

239 Speaker: Joan Vallvé ()
Title: Active Pose SLAM with RRT*
Abstract:

We propose a novel method for robotic exploration that evaluates paths that minimize both the joint path and map entropy per meter traveled. The method uses Pose SLAM to update the path estimate, and grows an RRT* tree to generate the set of candidate paths. This action selection mechanism contrasts with previous appoaches in which the action set was built heuristically from a sparse set of candidate actions. The technique favorably compares agains the classical frontier-based exploration and other Active Pose SLAM methods in simulations in a common publicly available dataset.

Links: Speaker info



238 Speaker: Adrià Colomé ()
Title: A Friction-Model-Based Framework for Reinforcement Learning of Robotic Tasks in Non-Rigid Environments
Abstract:

Learning motion tasks in a real environment with deformable objects requires not only a Reinforcement Learning (RL) algorithm, but also a good motion characterization, a preferably compliant robot controller, and an agent giving feedback for the rewards/costs in the RL algorithm. In this paper, we unify all these parts in a simple but effective way to properly learn safety-critical robotic tasks such as wrapping a scarf around the neck (so far, of a mannequin). We found that a suitable compliant controller ought to have a good Inverse Dynamic Model (IDM) of the robot. However, most approaches to build such a model do not consider the possibility of having hystheresis of the friction, which is the case for robots such as the Barrett WAM. For this reason, in order to improve the available IDM, we derived an analytical model of friction in the seven robot joints, whose parameters can be automatically tuned for each particular robot. This permits compliantly tracking diverse trajectories in the whole workspace. By using such friction-aware controller, Dynamic Movement Primitives (DMP) as motion characterization and visual/force feedback within the RL algorithm, experimental results demonstrate that the robot is consistently capable of learning tasks that could not be learned otherwise.

Links: Speaker info

237 Seminar 16-4-2015

Speaker: Stuart Maggs ()
Title: Minibuilders small robot 3D printing large scale objects, automating the construction site
Abstract:

The seminar will focus on current and future applications for robotics within the construction industry. Specifically the concept of Minibuilders, small robots 3D printing structures much larger than themselves. Swapping one large robot for a number of smaller, agile specialised robots, working in coordination towards a single structural outcome.

Links: Minibuilders info

236 Seminar 9-4-2015

Speaker: Ricardo Tellez ()
Title: Robots that build concepts I: building the concept of space
Abstract:

How do we create concepts? How can we design a robot that builds concepts? In this seminar I will show you a theoretical framework in which a naive robot, without any previous knowledge of the environment or of its body, can autonomously build concepts about the world or about itself. Specifically I will show how the robot can build the concept of space just by taking into account its sensorimotor flow, indicating the full dependence that perception has on action.

Links: Speaker info

235 Seminar 16-3-2015

Speaker: Keith Clark ()
Title: Programming Robotic Agents: A Multi-tasking Teleo-Reactive Approach
Abstract:

This talk will present a multi-threaded/multi-tasking message communicating robotic agent architecture in which the concurrently executing tasks are programmed in TeleoR, a major extension of Nilsson’s Teleo-Reactive Procedures (TR) language for robotic agents. TeleoR programmed tasks are robust and opportunistic, redoing or skipping actions as appropriate. This makes them well suited to robot/robot or human/robot co-operative tasks. TeleoR’s most important extension of TR is the concept and use of task atomic procedures to control the deadlock and starvation free sharing of multiple robotic resources by an agent’s concurrent tasks, as illustrated in this video

Links: Speaker info

234 Seminar 12-3-2015

Speaker: Arturo Ribes ()
Title: Active Learning of Object and Body Models with Time Constraints on a Humanoid Robot
Abstract:

We propose an active learning approach applied to a music performance imitation scenario. The humanoid robot iCub listens to a human performance and then incrementally learns to use a virtual musical instrument in order to imitate the given sequence. This is achieved by first learning a model of the instrument, needed to locate where the required sounds are heard in a virtual keyboard layed out in a tactile interface. Then, a model of its body affordances is also learnt, which serves to establish the likelihood of success of the actions needed to imitate the sequence of sounds and to correct the errors made by the underlying kinematic controller. It also uses self-evaluation stages to provide feedback to the human instructor, which can be used to guide its learning process.

Links: Speaker info

233 Seminar 2-3-2015

Speaker: Gerard Pons-Moll ()
Title: Modeling Humans in Motion: Capture and Animation
Abstract:

Not disclosed.

Links: Speaker info

232 Seminar 12-2-2015

Speaker: Adrià Colomé ()
Title: Reinforcement Learning of Robotic Tasks: Policy Search with Movement Primitives
Abstract:

Policy Search is nowadays the most used Reinforcement Learning approach to learn robot motion tasks. In this seminar, I will explain the process of learning a robotic task: why Policy search is used, how are trajectories represented, executed and learned, and some of the directions the research community is taking in this field.

Links: Speaker info

231 Seminar 29-1-2015

Speaker: Hilario Tomé ()
Title: Whole body control using Robust & Online hierarchical quadratic optimization
Abstract:

Recently, several formulations have been proposed to add inequality constraints to multi objective prioritized optimization problems. How to solve this problems with only equality constraints is a well known topic in robotics. Inequality constraints behave as equalities when they reach their bounds, so ideally we shouldn’t bother about them before we reach them. How we take them into account and what to do when they are reached affects drastically the computational effort of solving the problem. We present and derive an efficient way to deal with these problems using an off the shelf Quadratic Programming solver and choosing an appropriate solving strategy. We finally apply the proposed method to do whole body control in real time on a humanoid robot with 44 dof using a hierarchy of objectives.

Links: PAL Robotics, Video

230 Seminar 18-12-2014

Speaker: Vicenç Gomez ()
Title: Towards Kullback-Leibler control for robotics
Abstract:

Kullback-Leibler control problems define a general class of stochastic optimal control problems for which the controls and the cost function are restricted in a way that makes the Hamilton-Jacobi-Bellman equation linear and therefore more efficiently solvable. However, direct applicability of this theory to real robotics problems is limited because of two main reasons: first, current theory can only be applied to learn open-loop controllers and second, current computational methods do not scale well to high dimensional systems. I will briefly describe the theory behind this class of problems and present some recent results towards its application to real robotics systems.

Links: Speaker info

229 Seminar 4-12-2014

Speaker: Adriàn Peñate ()
Title: LETHA: Learning from High Quality Inputs for 3D Pose Estimation in Low Quality Images
Abstract:

We introduce LETHA (Learning on Easy data, Test on Hard), a new learning paradigm consisting of building strong priors from high quality training data, and combining them with discriminative machine learning to deal with low-quality test data. Our main contribution is an implementation of that concept for pose estimation. We first automatically build a 3D model of the object of interest from high-definition images, and devise from it a pose-indexed feature extraction scheme. We then train a single classifier to process these feature vectors. Given a low quality test image, we visit many hypothetical poses, extract features consistently and evaluate the response of the classifier. Since this process uses locations recorded during learning, it does not require matching points anymore. We use a boosting procedure to train this classifier common to all poses, which is able to deal with missing features, due in this context to self-occlusion. Our results demonstrate that the method combines the strengths of global image representations, discriminative even for very tiny images, and the robustness to occlusions of approaches based on local feature point descriptors.

Links: Speaker info

228 Seminar 20-11-2014

Speaker: Enric Celaya ()
Title: Online EM with Weight-Based Forgetting
Abstract:

The expectation-maximization (EM) algorithm is an iterative procedure to estimate the parameters of a finite mixture so as to maximize the likelihood of a set of unlabelled data. Its primary use is classification of data, but it can also been used for other tasks such as probability density estimation, regression, or function approximation. The EM algorithm is a batch algorithm, so that all data must be processed at each iteration. When the dataset to be processed is large, or when data are supplied as a stream, the EM algorithm becomes impractical and incremental or online versions of the algorithm are more convenient. Sato & Ishii (2000) developed an online EM algorithm in which a time-dependent discount factor was introduced to forget the influence of old estimations obtained with earlier, inaccurate estimators. In their approach, forgetting is uniformly applied to the estimators of each mixture component at each iteration, irrespective of the weight attributed to each component for the current data sample. This causes an excessive forgetting in the less frequently sampled components that may cause instabilities in the estimations. To address this problem we propose a modification of the algorithm that involves a weight-dependent forgetting, specific for each mixture component, by which old estimations are only forgotten in accordance with the actual weight used to replace them with new data. A comparison of the weight-independent versus the weight-dependent approach shows that this last improves the accuracy of the estimation and exhibits a much greater stability.

Links: Speaker info

227 Seminar 13-11-2014

Speaker: Angel Santamaria ()
Title: Task Priority Control for Aerial Manipulation
Abstract:

This talk presents a task oriented control strategy for aerial vehicles equipped with a manipulator. A camera is attached to the end-effector of the manipulator to perform a primary task consisting on visual servoing towards a desired target. Over-actuation of the whole quadrotor-arm system is exploited to achieve secondary velocity tasks. One subtask is proposed to horizontally stabilize the platform during flight by aligning the arm center of gravity with the quadrotor gravitational vector. The arm singularities and manipulability are addressed by another subtask that leads the arm to a preferable configuration, and also takes into account the arm joint limits. The performance of the whole visual servo and secondary tasks control scheme is shown in a Robot Operating System (ROS) implementation.

Links: Speaker info

226 Seminar 23-10-2014

Speaker: Aleix Rull ()
Title: On Generalized Dual Euler Angles
Abstract:

This paper first explores the generalization of Euler angles to the case in which the rotation axes are not necessarily members of an orthonormal triad, and presents a concise solution to their computation that relies on the calculation of standard Euler angles. Then, this generalization is taken one step further by introducing translations, that is, by defining generalized Euler angles about screw axes using a variation of the principle of transference that avoids the use of dual numbers. As an example, the obtained formulation is applied to solve the inverse kinematics of a 3C manipulator.

Links: Speaker info

225 Seminar 22-7-2014

Speaker: Federico Sukno ()
Title: 3D Facial Geometry and Dynamics for Behavior Analysis
Abstract:

In this talk, after a short introduction of my trajectory, I will briefly discuss the wide applications of facial behavior analysis and the current trend to encode emotion information. Then, I will concentrate on my most recent contributions to automatic facial landmarking in 3D, whose main building block an algorithm integrating combinatorial search on the responses from feature detectors, with statistical inference. A key assumption of our approach is that some landmarks might not be accurately identified by the feature detectors. While many approaches try to fight this by computing more features or analyzing more detections, we found that it is more accurate to omit the information from unreliable landmarks and infer their location statistically. As a byproduct, our approach is inherently tolerant to occlusions.

The core algorithm is complemented by a framework to objectively compare the suitability of different local descriptors for the localization of specific landmarks, in terms of their expected accuracy and working range (i.e. the neighborhood size where they aremost useful around the given target). Differently from precision-recall curves, these measures are landmark specific and show that, in general, no descriptor is suitable to tackle all landmarks typically targeted with optimal accuracy. Thus, combinations of descriptors are required, which we tackled by designing an extension to 3D Shape Contexts based on asymmetry patterns, whose most salient characteristic is its efficiency to generate pools of descriptors at relatively low computational cost.

An interesting outcome of our research on landmark localization was to highlight the lack of consistency of the manual annotations currently available for public databases that are widely used. In contrast to traditional measures of accuracy, such as inter- and intra-observer variability, we base our analysis on the consistency of annotations by comparing the inter-landmark distances of replicates (i.e. different scans from the same individual). We objectively showed that manual annotations currently available are suboptimal and an that appropriately trained algorithm, as the one described in this talk, can objectively outperform them for a majority of landmarks in terms of repeatability.

Links: Speaker info

224 Seminar 17-7-2014

Speaker: Pablo García-Amorena ()
Title: Testing Convolutional Neural Networks against hand-designing local features for Image Segmentation
Abstract:

In the Computer Vision Laboratory (CVLAB), at École Polytechnique Fédérale de Lausanne (EPFL), most of its algorithms for Biomedical Image Segmentation depend on hand-designing local features for classification purposes. A competing approach is to rely on Convolutional Neural Networks, an algorithm in which the CVLAB has little experience but would like to test for comparison purposes. The goal of the project is to understand what they can do but also what they can't do and what hand-designing features are still good for. To achieve it, the project starts with a phase of documentation, in order to get familiarized with Neural Networks in general and Convolutional Neural Networks in particular. The second part consists of a series of experiments with a Convolutional Neural Networks algorithm, so as to analyze its performance with different types of data and several improvements coming from the most recent state-of-the-art techniques. Finally, the project summarizes all the experiments done with the CLVAB biomedical data and makes a comparison with its hand-designing local features, in order to find out about their goodness and utility.

Links: Lab info

223 Seminar 9-7-2014

Speaker: Jeremy Wyatt ()
Title: Robots That Know What They Know
Abstract:

One of the long standing challenges at the interface between AI and Robotics is enabling robots to behave robustly in the face of incompleteness and uncertainty. In this talk I will describe the overall approach I have taken to this problem over the past ten years. Key to the approach is to model the world in ways that enables robust extrapolation to new cases, and to plan action sequences taking into account their information effects. I will structure the talk into two halves. In the first half I will give an overview, composed of three short parts: i) epistemic and assumptive planning in mobile robots for object search; ii) decision theoretic models of gaze control; ii) grasping novel objects under uncertainty. Half way through I'll let the audience vote for which part you want me to cover in more detail in the second half of the talk.

Links: Speaker info

222 Seminar 17-6-2014

Speaker: Carlos Rosales ()
Title: Active Gathering of Friction Coefficient (dynamic) and Inertial Values from Objects
Abstract:

Properties extracted from objects include shape, color, weight, texture, coefficient of friction, inertial values, degrees of freedom, among many others. The challenge in extracting them is present at hardware and software levels, particularly in designing robotic systems capable of effectuating desired exploratory strategies, of acquiring sufficient data to estimate the property, and having a robust and useful representation of it. The predominant properties used in robotic applications are shape and color due to the extensive use of cameras and the large computer vision field. However, others such as the friction coefficient and inertial values are relevant, for instance, for object grasping and manipulation and object recognition as additional channels of information. These two properties are hard to estimate using vision only, and contact-based procedures are advised.

We present two ongoing works to actively gather these two mechanical-related properties. The first one deals with the object shape and the friction coefficient extraction. The data comes from an RGB-D sensor and an intrinsic tactile sensor mounted on a 7 d.o.f. arm as the exploratory probe, and it is fed into a Gaussian Process as a single probabilistic representation. The exploratory strategy exploits the shape model to generate paths over the surface, which are followed by the probe to measure the friction coefficient. The second work tackles the gathering of the inertial values and its application to in-hand object pose estimation. The inertial values are leveraged from rigid body dynamics by grasping the object with a hand equipped with intrinsic tactile sensors at the fingertips and mounted on a 7 d.o.f. arm. The weight and principal values of inertia are used as a feature vector for recognition, and as a result of its computation, provided a database that associate shape and inertial values, it is also possible to estimate the in-hand object pose.

Links: Speaker info

Seminar 22-5-2014

221 Speaker: Joan Vallvé ()
Title: Dense Entropy Decrease Estimation for Mobile Robot Exploration
Abstract:

We propose a method for the computation of entropy decrease in C-space. These estimates are then used to evaluate candidate exploratory trajectories in the context of autonomous mobile robot mapping. The method evaluates both map and path entropy reduction and uses such estimates to compute trajectories that maximize coverage whilst minimizing localization uncertainty, hence reducing map error. Very efficient kernel convolution mechanisms are used to evaluate entropy reduction at each sensor ray, and for each possible robot position and orientation, taking frontiers and obstacles into account. In contrast to most other exploration methods that evaluate entropy reduction at a small number of discrete robot configurations, we do it densely for the entire C-space. The computation of such dense entropy reduction maps opens the window to new exploratory strategies. In this paper we present two such strategies. In the first one we drive exploration through a gradient descent on the entropy decrease field. The second strategy chooses maximal entropy reduction configurations as candidate exploration goals, and plans paths to them using RRT*. Both methods use PoseSLAM as their estimation backbone, and are tested and compared with classical frontier-based exploration in simulations using common publicly available datasets.

Links: Speaker info, ICRA-14.


220 Speaker: Adrian Amor ()
Title: On-Board Real-Time Pose Estimation for UAVs Using Deformable Visual Contour Registration
Abstract:

We present a real time algorithm for estimating the pose of non-planar objects on which we have placed a visual marker. It is designed to overcome the limitations of small aerial robots, such as slow CPUs, low image resolution and geometric distortions produced by wide angle lenses or viewpoint changes. The method initially registers the shape of a known marker to the contours extracted in an image. For this purpose, and in contrast to state-of-the art, we do not seek to match textured patches or points of interest. Instead, we optimize a geometric alignment cost computed directly from raw polygonal representations of the observed regions using very simple and efficient clipping algorithms. Further speed is achieved by performing the optimization in the polygon representation space, avoiding the need of 2D image processing operations. Deformation modes are easily included in the optimization scheme, allowing an accurate registration of different markers attached to curved surfaces using a single deformable prototype. Once this initial registration is solved, the object pose is retrieved using a standard PnP approach.

Links: Speaker info, ICRA-14.

Seminar 15-5-2014

219 Speaker: Farzad Husain ()
Title: Realtime Tracking and Grasping of a Moving Object from Range Video
Abstract:

In this paper we present an automated system that is able to track and grasp a moving object within the workspace of a manipulator using range images acquired with a Microsoft Kinect sensor. Realtime tracking is achieved by a geometric particle filter on the affine group. Based on the tracked output, the pose of a 7-DoF WAM robotic arm is continuously updated using dynamic motor primitives until a distance measure between the tracked object and the gripper mounted on the arm is below a threshold. Then, it closes its three fingers and grasps the object. The tracker works in real-time and is robust to noise and partial occlusions. Using only the depth data makes our tracker independent of texture which is one of the key design goals in our approach. An experimental evaluation is provided along with a comparison of the proposed tracker with state-of-the-art approaches, including the OpenNI-tracker. The developed system is integrated with ROS and made available as part of IRI's ROS stack.

Links: Speaker info, ICRA-14


218 Speaker: Gonzalo Ferrer ()
Title: Behavior Estimation for a Complete Framework for Human Motion Prediction in Crowded Environments
Abstract:

In the present work, we propose and validate a complete probabilistic framework for human motion prediction in urban or social environments. Additionally, we formulate a powerful and useful tool: the human motion behavior estimator. Three different basic behaviors have been detected: Aware, Balanced and Unaware. Our approach is based on the Social Force Model (SFM) and the intentionality prediction BHMIP. The main contribution of the present work is to make use of the behavior estimator for formulating a reliable prediction framework of human trajectories under the influence of dynamic crowds, robots, and in general any moving obstacle. Accordingly, we have demonstrated the great performance of our long-term prediction algorithm, in real scenarios, comparing to other prediction methods.

Links: Speaker info, ICRA-14


217 Speaker: David Martinez ()
Title: Active Learning of Manipulation Sequences
Abstract:

We describe a system allowing a robot to learn goal-directed manipulation sequences such as steps of an assembly task. Learning is based on a free mix of exploration and instruction by an external teacher, and may be active in the sense that the system tests actions to maximize learning progress and asks the teacher if needed. The main component is a symbolic planning engine that operates on learned rules, defined by actions and their pre- and postconditions. Learned by model-based reinforcement learning, rules are immediately available for planning. Thus, there are no distinct learning and application phases. We show how dynamic plans, replanned after every action if necessary, can be used for automatic execution of manipulation sequences, for monitoring of observed manipulation sequences, or a mix of the two, all while extending and refining the rule base on the fly. Quantitative results indicate fast convergence using few training examples, and highly effective teacher intervention at early stages of learning.

Links: Speaker info, ICRA-14.

216 Seminar 8-5-2014

Speaker: Sergi Foix ()
Title: Coding leaf exploratory tasks under 3D occupancy-based models
Abstract:

In this talk I will present some recent advances on my research in active viewpoint planning for gathering data from an unexplored free-form and non-rigid 3D complex scenario. The aim of this research is to provide a general task-oriented approach based on information-gain maximization that easily deals with such a problem. In particular for this talk, I will show the advances on how to encode viewpoint planning in 3D occupancy models with the purpose of identifying available probing points in leaves. In overall terms, the approach consists of repetitively ranking a given set of possible viewpoints, based on their task-related gains, and then move to the best-ranked vantage point until the exploratory task termination criterion is achieved.

Links: Speaker info

215 Seminar 20-3-2014 (Cancelled by the speaker)

Speaker: Babette Dellen ()
Title: Not available
Abstract:

Not provided

Links: Speaker info

214 Seminar 6-3-2014

Speaker: Andreu Corominas ()
Title: Inertial Measurent Units and their Application to Mobile Robot Localization
Abstract:

In this talk we will first review which data provides an Inertial Measurement Unit (IMU), and how it is processed to be used in localization problems, focusing on aspects such as gravity cancellation and bias effects. Afterwards, we will present the work carried out in the context of the PipeGuard FP7 European project. In this later case IMU and encoder measurements were used to localize a sewer pipe inspection vehicle, following an iterative minimization over a sliding window of platform states. Experimental results will be shown, pointing out the strongest points of IMU devices, but analysing why they can't be alone in a localization application.

Links: Speaker info, Paper

213 Seminar 13-2-2014

Speaker: Jean Marc Montanier ()
Title: A proposition for the design and control of swarm of robots
Abstract:

Advanced mobile robots today provide for the achievement of a variety of complex tasks, such as warehouse management (Kiva company), space exploration (NASA missions) or car manufacturing (industrial robots). The Spirit and Opportunity robots sent by NASA to Mars are such robots, evaluated at a staggering $625 million. Although an extreme case, with respect to cost, advanced mobile robots may be said to be, not only expensive, but also limited by a single point of failure i.e. one single robot.

The swarm robotic approach focuses on solving varied and complex tasks through collective behaviours emerging from the interaction between autonomous simple. Such agents are therefore simpler in their design and therefore more robust and affordable. Moreover the use of a large number of robots removes the single point of failure. However experiments in Swarm robotics are facing hardware and software challenges. On the hardware side, either the cost of a robot is prohibitive or the robot is limited in its functionalities. On the software side, it is difficult to design controllers for these robots, as a large number of interactions has to be taken into account.

I will present in more details the difficulties in designing a robot for swarm robotic and a solution developed at the CRAB Lab of NTNU. I will also present the difficulties in designing controller for these robots and the main avenues of research I'm exploring.

Links: Speaker info

212 Seminar 6-2-2014

Speaker: Eduard Trulls ()
Title: Bottom-up segmentation for enhanced feature matching
Abstract:

The combination of segmentation and recognition is a long-standing problem in computer vision. In this talk we will present an approach to exploit segmentation to construct appearance descriptors (e.g. SIFT) that can deal with occlusion and background changes, by downplaying measurements coming from pixels that are likely to belong to a different region than that of the descriptor's center. We will also present our current work, extending the same approach to object recognition with deformable part-based models, using SLIC superpixels to 'clean up' the HOG features and separate foreground and background.

Links: Speaker info

211 Seminar 21-11-2013

Speaker: Norihiro Hagita ()
Place: FME conference room
Time: 12:00
Title: Robotics and Robotics Services in Smart Cities
Abstract:

Not disclosed.

Links: Speaker Info

210 Seminar 21-11-2013

Speaker: Viorela Ila ()
Place: IRI meeting room floor -1
Time: 11:00
Title: Blocks Rock! Block Matrix Framework for Nonlinear Least Squares
Abstract:

A large number of robotic, computer vision and computer graphics applications rely on efficiently solving the associated sparse linear systems. Simultaneous localization and mapping (SLAM), structure from motion (SfM), non-rigid shape recovery, and elastodynamic simulations are only few examples in this direction. In general, these problems are nonlinear and the solution can be approximated by incrementally solving a series of linearized problems. In some applications, the size of the system considerably affects the performance, especially when the sparsity is low. Exploiting the block structure of such problems, we developed very efficient solutions to manipulate block matrices within iterative nonlinear solvers. We proposed a scheme which combines the advantages of block-wise schemes convenient in both, numeric and structural matrix modification and element-wise, which are efficient in arithmetic operations. SLAM++ is a very efficient implementation of incremental nonlinear least squares, based on the sparse block matrix scheme. It is aimed for use in 3D reconstruction and robotics. The implementation was designed with hardware acceleration in mind. This is very important for large scale problems, which can efficiently run on wide scale of accelerators, ranging from DSPs and FPGAs to clusters of GPUs.

Links: Speaker Info

209 Seminar 7-11-2013

Speaker: Josep M. Porta ()
Title: The Cuik Suite: Motion Analysis of Closed-chain Multibody Systems
Abstract:

Many situations in Robotics require an effective analysis of the motions of a closed-chain mechanism. Despite appearing very often in practice (e.g. in parallel manipulators, reconfigurable robots, or molecular compounds), there is a lack of general tools to effectively analyze the complex configuration spaces of such systems. In this talk I will give an overview of the Cuik Suite, an open-source toolbox for motion analysis of general closed-chain mechanisms. The package can determine the motion range of the whole mechanism or of some of its parts, detect singular configurations leading to control or dexterity issues, or find collision- and singularity-free paths between given configurations. The toolbox is the result of several years of research and development within the Kinematics and Robot Design group and is available under GPLv3 license.

Links: CuikSuite, Speaker Info

Seminar 24-10-2013

208 Speaker: Gonzalo Ferrer ()
Title: Robot Companion: A Social-Force based approach with Human Awareness-Navigation in Crowded Environments
Abstract:

Robots accompanying humans is one of the core capacities every service robot deployed in urban settings should have. We present a novel robot companion approach based on the so-called Social Force Model (SFM). A new model of robot-person interaction is obtained using the SFM which is suited for our robots Tibi and Dabo. Additionally, we propose an interactive scheme for robot's human-awareness navigation using the SFM and prediction information. Moreover, we present a new metric to evaluate the robot companion performance based on vital spaces and comfortableness criteria. Also, a multimodal human feedback is proposed to enhance the behavior of the system. The validation of the model is accomplished throughout an extensive set of simulations and real-life experiments.

207 Speaker: Patrick Grosch ()
Title: A Bilinear Formulation for the Motion Planning of Non-Holonomic Parallel Orienting Platforms
Abstract:

This paper deals with the motion planning problem for parallel orienting platforms with one non-holonomic joint and two prismatic actuators which can maneuver to reach any three-degree-of-freedom pose of the moving platform. Since any system with two inputs and up to four generalized coordinates can always be transformed into chained form, this path planning problem can be solved using well-established procedures. Nevertheless, the use of these procedures requires a good understanding of Lie algebraic methods whose technicalities have proven a challenge to many practitioners who are not familiar with them. As an alternative, we show how by (a) properly locating the actuators, and (b) representing the platform orientation using Euler parameters, the studied path planning problem admits a closed-form solution whose derivation requires no other tools than ordinary linear algebra.

Links: IROS-13

206 Seminar 22-10-2013

Speaker: Horst Bunke ()
Title: A quadratic time approximation of graph edit distance
Abstract:

Graph edit distance is a powerful and flexible method for error-tolerant graph matching. Yet often it can only be calculated for small graphs due to its exponential time complexity. In this talk we propose a quadratic time approximation of graph edit distance based on the Hausdorff distance. In a series of experiments we analyze the performance of the proposed Hausdorff edit distance in the context of graph classification and compare it with a cubic time algorithm based on the assignment problem. Overall, the proposed Hausdorff edit distance shows a promising potential in terms of flexibility, efficiency, and accuracy.

Links: Speaker Info

205 Seminar 17-10-2013

Speaker: Aleksandar Jevtic ()
Title: 3D vision as interaction modality for mobile robots
Abstract:

The study of human-robot interaction (HRI) carries numerous challenges related to the concepts general to HRI and the specific use of robotic systems that interact with humans in a particular application domain. Mobile robots that operate in a home setting must be able to continuously track users and attend their calls for interaction. Recent release of affordable RGB-D sensors allowed development of new interaction modality that relies on real-time human body segmentation and can be applied in this domain. The proposed work, performed as a part of the EU-funded INTRO project, exploits 3D vision as input for intuitive interaction through person tracking, person following, gesture recognition and robot control. This seminar will introduce the algorithms and the challenges related to system integration and practical deployment of a RGB-D sensor on the autonomous mobile robot.

Links: Home page, INTRO project

204 Seminar 10-10-2013

Speaker: Arnau Ramisa ()
Title: FINDDD: A Fast 3D Descriptor to Characterize Textiles for Robot Manipulation
Abstract:

Most current depth sensors provide 2.5D range images in which depth values are assigned to a rectangular 2D array. In this paper we take advantage of this structured information to build an efficient shape descriptor which is about two orders of magnitude faster than competing approaches, while showing similar performance in several tasks involving deformable object recognition. Given a 2D patch surrounding a point and its associated depth values, we build the descriptor for that point, based on the cumulative distances between their normals and a discrete set of normal directions. This processing is made very efficient using integral images, even allowing to compute descriptors for every range image pixel in a few seconds. The discriminative power of our descriptor, dubbed FINDDD, is evaluated in three different scenarios: recognition of specific cloth wrinkles, instance recognition from geometry alone, and detection of reliable and informed grasping points.

Links: IROS-13

203 Seminar 26-6-2013 (11h)

Speaker: Pol Monsó ()
Title: Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks with Learned Shape Features
Abstract:

Immense amounts of high resolution data are now routinely produced thanks to recent advances in EM imaging. While a strong demand for automated analysis now exists, it is stifled by the lack of robust automatic 3D segmentation techniques. State-of-the-art Computer Vision algorithms designed to operate on natural 2D images tend to perform poorly when applied to EM image stacks for a number of reasons. The sheer size of a typical EM image stack renders many segmentation schemes intractable. Most approaches rely on local statistics that easily become confused when confronted with the noise and textures found within EM image stacks. The assumption that strong image gradients always correspond to object boundaries is violated by cluttered membranes belonging to numerous objects. In this work, we propose an automated graph partitioning scheme that addresses these issues. It reduces the computational complexity by operating on supervoxels instead of voxels, incor- porates global shape features capable of describing the 3D shape of the target objects, and learns to recognize the distinctive ap- pearance of true boundaries. Our experiments demonstrate that, when applied to segment mitochondria from neural tissue, our approach closely matches the performance of human annotators and outperforms a state-of-the-art 3D segmentation technique.

Links: Lab

202 Seminar 20-6-2013

Speaker: Montserrat Manubens ()
Title: Motion planning for 6-D manipulation with aerial towed-cable systems
Abstract:

Performing aerial 6-dimensional manipulation using flying robots is a challenging problem, to which only little work has been devoted. This paper proposes a motion planning approach for the reliable 6-dimensional quasi-static manipulation with an aerial towed-cable system. The novelty of this approach lies in the use of a cost-based motion-planning algorithm together with some results deriving from the static analysis of cable-driven manipulators. Based on the so-called wrench-feasibility constraints applied to the cable tensions, as well as thrust constraints applied to the flying robots, we formally characterize the set of feasible configurations of the system. Besides, the expression of these constraints leads to a criterion to evaluate the quality of a configuration. This allows us to define a cost function over the configuration space, which we exploit to compute good-quality paths using the T-RRT algorithm. As part of our approach, we also propose an aerial towed-cable system that we name the FlyCrane. It consists of a platform attached to three flying robots using six fixed-length cables. We validate the proposed approach on two simulated 6-D quasi-static manipulation problems involving such a system, and show the benefit of taking the cost function into account for such motion planning tasks.

Links: Speaker Info, RSS-13

201 Seminar 18-6-2013

Speaker: Juan Solà ()
Title: Rich and high dynamic pose estimation for naturally unstable robots (visual-inertial sensor fusion)
Abstract:

This presentation is devoted to the real-time estimation of the pose (position and orientation in 3D space) of high-dynamic and naturally unstable robots such as aerial drones, humanoid robots, or ground robots running at very high speeds on rough terrain. The key point is the inclusion of inertial measurements fused with vision, leading to a quite elegant replica of what constitutes the oculo-vestibular system of superior animals, key for their sense and control of motion and equilibrium. The system draws on monocular, EKF-based simultaneous localization and mapping (SLAM). It was initially motivated by the need of real-time, rich and accurate pose estimates to be used within the control loop of the humanoid robot HRP2 at LAAS-CNRS, although it is equally useful in any device requiring fast pose updates with information of the gravity direction, crucial in naturally unstable robots of different nature. In particular, the outcome of the visual-inertial fusion algorithm is a vector of position, orientation, velocity, angular velocity, acceleration, gravity vector, and sensor biases, which is updated at frequencies ranging from 100Hz up to several kHz depending only on the IMU data-rate. These estimates are both globally and locally consistent thanks to the fusion of the complementary characteristics of the visual and inertial sensors used.

Links: Speaker Info

200 Seminar 13-6-2013

Speaker: Edgar Simo ()
Title: A Joint Model for 2D and 3D Pose Estimation from a Single Image
Abstract:

We introduce a novel approach to automatically recover 3D human pose from a single image. Most previous work follows a pipelined approach: initially, a set of 2D features such as edges, joints or silhouettes are detected in the image, and then these observations are used to infer the 3D pose. Solving these two problems separately may lead to erroneous 3D poses when the feature detector has performed poorly. In this paper, we address this issue by jointly solving both the 2D detection and the 3D inference problems. For this purpose, we propose a Bayesian framework that integrates a generative model based on latent variables and discriminative 2D part detectors based on HOGs, and perform inference using evolutionary algorithms. Real experimentation demonstrates competitive results, and the ability of our methodology to provide accurate 2D and 3D pose estimations even when the 2D detectors are inaccurate.

Links: Speaker Info, CVPR-13

199 Seminar 30-5-2013

Speaker: Alex Goldhoorn ()
Title: Methods to play Hide-and-Seek as a Mobile Robot
Abstract:

Hide-and-seek is a good game to study Human Robot Interaction; there are cognitive as well as engineering approaches to play the game as a robot. We have focused on the latter using different models and strategies to play as a seeker. Tests have been done extensively in simulations, and partly in real world experiments with Dabo. We introduce a new fast heuristic method for the seeker and a more complex method that uses MOMDP (Mixed Observability Markov Decision Processes) models to learn a good policy to be applied by the seeker. Even though MOMDPs reduce the computational cost of POMDPs (Partially Observable MDPs), they still have a high computational complexity which is exponential with the number of states. For the hide-and-seek game, the number of states is directly related to the number of grid cells. As an alternative to off-line MOMDP policy computation with the complete grid fine resolution, we have devised a two-level MOMDP, where the policy is computed on-line at the top level with a reduced number of states independent of the grid size.

Links: Speaker Info

198 Seminar 21-5-2013

Speaker: Marcela Riccillo ()
Title: Innovations in Humanoid Robots 2013
Abstract:

Currently Robotics reached vehicular traffic through autonomous vehicles circulating several streets in the world. This advance was greatly influenced by the 2007 USA DARPA Grand Challenge where robot car teams participated from all world. Now, there are a new 2013-2014 Grand Challenge, but this time the leap will be on the Humanoid Robots. In this talk we will know about robotic trends and the principal players of the Robotics in the world through this big competition that could change the scope of the humanoids as we know.

Links: Speaker Info

197 Seminar 2-5-2013

Speaker: Angel Santamaria ()
Title: Uncalibrated Image-Based Visual Servoing
Abstract:

This paper develops a new method for uncalibrated image-based visual servoing. In contrast to traditional image-based visual servo, the proposed solution does not require a known value of camera focal length for the computation of the image Jacobian. Instead, it is estimated at run time from the observation of the tracked target. The technique is shown to outperform classical visual servoing schemes in situations with noisy calibration parameters and for unexpected changes in the camera zoom. The method's performance is demonstrated both in simulation experiments and in a ROS implementation of a quadrotor servoing task. The developed solution is tightly integrated with ROS and is made available as part of the IRI ROS stack.

Links: Speaker Info, ICRA-13

196 Seminar 25-4-2013

Speaker: Adrià Colomé ()
Title: External Force Estimation During Compliant Robot Manipulation
Abstract:

This paper presents a method to estimate external forces exerted on a manipulator during motion, avoiding the use of a sensor. The method is based on task-oriented dynamics model learning and a robust disturbance state observer. The combination of both leads to an efficient torque observer that can be incorporated to any control scheme. The use of a learning-based approach avoids the need of analytical models of joints' friction or Coriolis dynamics effects.

Links: Speaker Info, ICRA-13

195 Seminar 18-4-2013

Speaker: Udo Frese ()
Title: [+|-manifolds and their use in estimation algorithms
Abstract:

A [+]-manifold, just as a manifold, is a subspace S of R^s which can have a globally very complicated structure but locally behaves like R^n. An example is 3-D orientation. This structure is encapsulated in an operator [+]:S*R^n->S that allows to apply a local vector valued change to an element and an inverse operator [-]:S*S-->R^n. WIth these operators most estimation algorithms can work on [+]-manifolds, mostly by replacing + with [+] and - with [-].
The talk gives overview and intuition, why [+]-manifolds are a useful concept, how they are axiomatized and how they are used in estimation algorithms such as least-squares or UKF.

Links: More Info, Speaker Info

194 Seminar 11-4-2013

Speaker: Babette Dellen ()
Title: Learning object-action relations from observation with semantic event chains
Abstract:

Recognizing manipulations performed by a human and the transfer and execution of this by a robot is a difficult problem. We address this by introducing a novel representation of the relations between objects at decisive time points during a manipulation. Thereby, we encode the essential changes in a visual scenery in a condensed way such that a robot can recognize and learn a manipulation without prior object knowledge. To achieve this we continuously track image segments in the video and construct a dynamic graph sequence. Topological transitions of those graphs occur whenever a spatial relation between some segments has changed in a discontinuous way and these moments are stored in a transition matrix called the (SEC). We demonstrate that these time points are highly descriptive for distinguishing different manipulations. Employing simple sub-string search algorithms, semantic event chains can be compared and type-similar manipulations can be recognized with high confidence. As the approach is generic, statistical learning can be used to find the archetypal SEC of a given manipulation class. The performance of the algorithm is demonstrated on a set of real videos showing hands manipulating various objects and performing different actions. In experiments with a robotic arm, we show that the SEC can be learned by observing human manipulations, transferred to a new scenario, and then reproduced by the machine.

Links: Speaker Info

193 Seminar 7-3-2013

Speaker: Montserrat Manubens ()
Title: Navigating the wrench-feasible C-space of cable-driven hexapods
Abstract:

Motion paths of cable-driven hexapods must carefully be planned to ensure that the lengths and tensions of all cables remain within acceptable limits, for a given wrench applied to the platform. The cables cannot go slack –to keep the control of the platform– nor excessively tight –to prevent cable breakage– even in the presence of bounded perturbations of the wrench. This talk presents a path planning method that accommodates such constraints simultaneously. Given two configurations of the platform, the method attempts to connect them through a path that, at any point, allows the cables to counteract any wrench lying inside a predefined uncertainty region. The resulting C-space is placed in correspondence with a smooth manifold, which allows defining a continuation strategy to search this space systematically from one configuration, until the second configuration is found, or path non-existence is proved by exhaustion of the search. The approach is illustrated on the NIST Robocrane hexapod, but it remains applicable to general cable-driven hexapods, either to navigate their full six-dimensional C-space, or any of its slices.

Links: Speaker Info

192 Seminar 21-2-2013

Speaker: Horst Bunke ()
Title: Bridging the Gap Between Structural and Statistical Pattern Recognition
Abstract:

The discipline of pattern recognition is traditionally divided into the statistical and the structural approach. Statistical pattern recognition is characterized by representing objects by means of feature vectors, while the structural approach uses symbolic data structures, such as strings, trees, and graphs. In the current talk we focus on graphs for object representation. When comparing graph representations with feature vectors, one notices an increased flexibility and representational power provided by graphs. On the other hand, the domain of graphs lacks mathematical operations needed to build pattern recognition and machine learning algorithms and consequently, there is a shortage of suitable tools for graph classification, clustering, and related tasks.
In this talk, we review some advances in the field of graph-based pattern recognition that aim at making algorithmic tools originally developed in statistical pattern recognition available for graphs. We will focus on graph embedding and discuss relations with graph kernels. As a matter of fact, graph embedding provides us with an elegant and systematic way to make the complete arsenal of statistical pattern recognition tools available for graphs. The talk will also address computational complexity problems that arise with graph representations, discuss how they may possibly be overcome, and give an application example.

Links: Speaker Info

191 Seminar 31-1-2013

Speaker: Adrián Peñate ()
Title: 3D pose estimation in low quality images
Abstract:

Obtaining object pose in low quality images cannot be achieved using the classical point features approach. To solve this cases we propose a method that learns on high quality controlled data the inner structure of the object. We will show that a strong understanding of the object makes possible recognition of it when bad or partial data is provided. To understand this inner structure we will use 3D data and high resolution images annotated with the pose of the object. Our main contributions are a new procedure to perform 3D training and a new boosting technique that is able to handle 3D visibility of the features. We compare against state-of-the-art methods for recognition in low quality data, we outperform them substantially.

Links: Speaker Info

190 Seminar 16-1-2013

Speaker: Nicola Rebagliati ()
Title: Some Recent Results On Graph Matching Problems
Abstract:

In this talk I will present, at a high level, some recent advances on two fundamental graph matching problems: the graph edit distance and the computation of a median graph. In the first part, I will present a new method to compute the Graph Edit Distance between two attributed graphs which is based on Dominant Sets, a notion of cluster recently introduced. In the second part, I will show new theoretical results which link the problem of finding the Median Graph of a set of graphs to the Common Labelling problem. These are joint works with Albert Solé Ribalta, Francesc Serratosa and Marcello Pelillo.

Links: Speaker Info

189 Seminar 20-12-2012

Speaker: Jordi Sánchez ()
Title: Social skills on Humanoid robot NAO
Abstract:

Humanoids expected to collaborate with people should be able to interact with them in the most natural way. This involves significant perceptual, communication, and motor processes, operating in a coordinated fashion. Consider a social gathering scenario where a humanoid is expected to possess certain social skills. It should be able to explore a populated space, to localize people and to determine their status. Humans appear to solve these tasks routinely by integrating the often complementary information provided by multi sensory data processing.
In the talk will be presented some applications to achieve these goals. A sceneflow and a temporal version of a stereo seed growing algorithm are developed to provide to the robot information about the environment. In order to give to the robot some commands, an action recognition method based on the Bag-of-Words framework is implemented using a descriptor based on the 3D data and auditory information. Finally to make the interaction more real we would like that the robot identifies when a person is speaking and direct the head towards this person. This is achieved also using visual and auditory data fused by a Gaussian mixture model.

Links: Speaker Info

188 Seminar 13-12-2012

Speaker: Iván Huerta ()
Title: Unsupervised Multi-Appearance Object Learning from Motion Cues
Abstract:

Current object class detectors learn models from generic images taken from different viewpoints and lighting conditions but using ground truth data. A lot of effort is usually devoted to manual labelling, since the goal is to generate a robust object model able to work for different appearances and aspects. Unfortunately, manual labelling is expensive, specially in long image sequences. Also, object detectors trained for general scenes usually exhibit poor performance under uncontrolled conditions. Finally, there are sequences where we are interested to detect particular objects for which there is not specific data, i.e. pre-trained detectors. In such cases, motion segmentation is still the most practical solution. In this paper, we propose an alternative solution based on a combination of motion segmentation and recent advances on weakly-supervised object detection. We propose a framework based on latent SVMs that, given a noisy initialization based on motion cues, is able to learn the correct position, the scale, and the appearance of all moving ob jects in the scene. We test our new approach on challenging sequences, and show that our detector outperforms motion segmentation as well as general object detectors.

Links: Speaker Info

187 Seminar 22-11-2012

Speaker: Gonzalo Ferrer ()
Title: Human Motion Prediction in Urban Environments
Abstract:

The deployment of service robots in urban settings is an open issue for the robotics community. Tackling the problem by only relying on a robust navigation is not enough, although this topic has grown enormously in the few past years. Thus, the understanding of human motion in urban environments is of extreme importance in order to adapt service robots to typical human environments and not in the contrary. Human motion prediction in indoor and outdoor scenarios is a key issue towards an intelligent robot navigation and human robot interaction in general. In thiss seminar, I will present a new human motion intentionality indicator, denominated Bayesian Human Motion Intentionality Prediction (BHMIP), which is a geometric-based long-term predictor. The BHMP is compared with other long-time prediction state of the art methods using two well known databases. Additionally, experiments in a real scenario are carried out including a set of volunteers walking in the presence of a mobile two-wheeled robot, to validate the overall performance of the BHMIP.

Links: Speaker Info

186 Seminar 8-11-2012

Speaker: Michael Villamizar ()
Title: Online Human-Assisted Learning Using Random Ferns
Abstract:

We present an Online Random Ferns (ORFs) classifier that progressively learns and builds enhanced models of object appearances. During the learning process, we allow the human intervention to assist the classifier and discard false positive training samples. The amount of human intervention is minimized and integrated within the online learning, such that in a few seconds, complex object appearances can be learned. After the assisted learning stage, the classifier is able to detect the object under severe changing conditions. The system runs at a few frames per second, and has been validated for face and object detection tasks on a mobile robot platform. We show that with minimal human assistance we are able to build a detector robust to viewpoint changes, partial occlusions, varying lighting and cluttered backgrounds.

Links: Speaker Info, ICPR-12

185 Seminar 18-10-2012

Speaker: Sergi Foix ()
Title: Information-gain view planning for free-form object reconstruction with a 3D ToF camera
Abstract:

Active view planning for gathering data from an unexplored 3D complex scenario is a hard and still open problem in the computer vision community. In this paper, we present a general task-oriented approach based on an information-gain maximization that easily deals with such a problem. Our approach consists of ranking a given set of possible actions, based on their task-related gains, and then executing the best-ranked action to move the required sensor.
An example of how our approach behaves is demonstrated by applying it over 3D raw data for real-time volume modelling of complex-shaped objects. Our setting includes a calibrated 3D time-of-flight (ToF) camera mounted on a 7 degrees of freedom (DoF) robotic arm. Noise in the sensor data acquisition, which is too often ignored, is here explicitly taken into account by computing an uncertainty matrix for each point, and refining this matrix each time the point is seen again. Results show that, by always choosing the most informative view, a complete model of a 3D free-form object is acquired and also that our method achieves a good compromise between speed and precision.

Links: Speaker Info

Seminar 4-10-2012

184 Speaker: Rafael Valencia ()
Title: Active Pose SLAM
Abstract:

We present an active exploration strategy that complements Pose SLAM and optimal navigation in Pose SLAM. The method evaluates the utility of exploratory and place revisiting sequences and chooses the one that minimizes overall map and path entropies. The technique considers trajectories of similar path length taking marginal pose uncertainties into account. An advantage of the proposed strategy with respect to competing approaches is that to evaluate information gain over the map, only a very coarse prior map estimate needs to be computed. Its coarseness is independent and does not jeopardize the Pose SLAM estimate. Moreover, a replanning scheme is devised to detect significant localization improvement during path execution. The approach is tested in simulations in a common publicly available dataset comparing favorably against frontier based exploration.

Links: Speaker Info, IROS-12

183 Speaker: Adrià Colomé ()
Title: Redundant Inverse Kinematics: Experimental Comparative Review and Two Enhancements
Abstract:

Motivated by the need of a robust and practical Inverse Kinematics (IK) algorithm for the WAM robot arm, we reviewed the most used closed-loop methods for redundant robots, analysing their main points of concern: convergence, numerical error, singularity handling, joint limit avoidance, and the capability of reaching secondary goals. As a result of the experimental comparison, we propose two enhancements. The first is to filter the singular values of the Jacobian matrix before calculating its pseudoinverse in order to obtain a more numerically robust result. The second is to combine a continuous task priority strategy with selective damping to generate smoother trajectories. Experimentation on the WAM robot arm shows that these two enhancements yield an IK algorithm that outperforms the reviewed state-of-the-art ones, in terms of the good compromise it achieves between time step length, Jacobian conditioning, multiple task performance, and computational time, thus constituting a very solid option in practice. This proposal is general and applicable to other redundant robots.

Links: Speaker Info, IROS-12

182 Seminar 27-9-2012

Speaker: Dimiter Zlatanov ()
Title: Singularities of mechanisms and robots
Abstract:

In certain configurations, usually referred to as singularities, the kinematic and static properties of mechanisms change dramatically. As these changes are often detrimental, the study of such singular configurations has been a major topic in the robotics literature. However, most of the work done concentrates on specific mechanisms and is motivated by the desire to avoid narrowly defined types of configurations. Even among specialists in robot kinetostatics, there is slight knowledge and often misunderstanding about what singularities are in general and what their effects can be. The talk will try to provide a simple but rigorous introduction to the topic. Particular attention will be given to lesser known types of singularity, exhibited by some parallel robots and other mechanisms with closed-loop kinematic chains.

Links: Speaker Lab

181 Seminar 21-9-2012

Speaker: Adnan Sljoka ()
Title: Counting for rigidity and flexibility - algorithms and applications to protein flexibility and linkage decompositions
Abstract:

In combinatorial characterization of rigidity one can simply count the vertices and edges (constraints) in a graph and its subgraphs to determine the rigidity and flexibility of a corresponding framework. The first complete combinatorial generalization for (generic) bar and joint frameworks in dimension 2 was confirmed by Laman in 1970. The 6|V|- 6 counting condition for 3-dimensional body-hinge and molecular structures, and a fast pebble game algorithm which tracks this count in the multigraph, have led to the development of the program FIRST, for rapid predictions of the rigidity/flexibility in proteins. In this talk we will show how our various extensions of the pebble game algorithm can be used to predict hinge motions in proteins, as well as some preliminary predictions for elusive allosteric communication - where binding on one portion of protein changes the shape or binding at a distance active site of the protein. We will demonstrate our rigidity-based allosteric communication on the important class of signalling proteins known as GPCRs. Time permitting, we will briefly discuss how our rigidity techniques and algorithms can be used to decompose a linkage into Assur graphs.

Links: Speaker Info

180 Seminar 26-6-2012 (at 16:00)

Speaker: Albert Cardona ()
Title: Neural circuit reconstruction from electron microscopy image volumes
Abstract:

Neural circuits consist of neuronal arbors that synapse onto each other. The pattern of connectivity specifies the function that a circuit implements. Electron microscopy is currently the only imaging modality capable of unambiguously resolving the smallest circuit elements, namely individual synapses and terminal dendrites. Technological developments in electron microscopy, driven by the needs of neuroscience--large volumes at nanometer resolution--make possible the imaging of complete brains of small animals, and large chunks of mammalian nervous tissue. Even small animals like the Drosophila larva contain tens of thousands of neurons and millions of synapses, and therefore manual reconstruction is not possible. The stumbling block in extracting neural circuits from EM image volumes is the lack of fast and reliable algorithms for the reconstruction of neuronal arbors in 3d and the labeling of synaptic contacts. In this talk, I will show how neurons and synapses look like in EM image volumes; demonstrate all that goes wrong with current methods, and outline ideas for the development of algorithms that imitate humans in their ability to overcome ambiguities originating in noise and data loss for the successful reconstruction of neural circuits in desirable time scales.

Links: Speaker Info

179 Seminar 21-6-2012

Speaker: Oriol Bohigas ()
Title: Planning Singularity-free Force-feasible Paths on the Stewart Platform
Abstract:

This paper provides a method for computing force-feasible paths on the Stewart platform. Given two configurations of the platform, the method attempts to connect them through a path that, at any point, allows the platform to counteract any external wrench lying inside a predefined six-dimensional region. In particular, the Jacobian matrix of the manipulator will be full rank along such path, so that the path will not traverse the f orward singularity locus at any point. The path is computed by first characterizing the force-feasible C-space of the manipulator as the solution set of a system of equations, and then using a higher-dimensional continuation technique to explore this set systematically from one configuration, until the second configuration is found. Examples are included that demonstrate the performance of the method on illustrative situations.

Links: Speaker Info, ARK-12

178 Seminar 14-6-2012

Speaker: Nicolás Rojas ()
Title: Distance-Based Formulations for the Position Analysis of Kinematic Chains
Time: 11:00
Abstract:

This seminar will be a rehearsal of Nico's PhD defense.

Links: Speaker Info

177 Seminar 7-6-2012

Speaker: Syed Farzad Husain ()
Title: Consistent depth video segmentation using adaptive surface models
Abstract:

We present a novel approach for the segmentation of 3-D point clouds into geometric surfaces using adaptive surface models. The intrinsic adaptation mechanism allows the segmentation to adapt to changing depth data, leading to stable, temporally coherent, and traceable segments. Starting from an initial configuration, the algorithm converges to a stable segmentation through a novel iterative split-and-merge procedure. By simply udpating the 3-D point cloud with the upcoming frame during the movie, the segmentation accommodates to the current data. This strategy further allows the formation of new segments during the video and the elimination of segments representing objects that are disappearing. We tested the method on a large variety of data acquired with different range imaging devices, including a structured-light sensor and a time-of-flight camera, and successfully segmented the videos into surfaces segments. We further demonstrate the feasibility of the approach using quantitative evaluations based on ground-truth data.

Links: Speaker Info

176 Seminar 30-5-2012

Speaker: Edgar Simo ()
Title: Kinematic Synthesis of Multi-fingered Robotic Hands for Finite and Infinitesimal Tasks
Abstract:

In this paper we present a novel method of designing multi-fingered robotic hands using tasks composed of both finite and infinitesimal motion. The method is based on representing the robotic hands as a kinematic chain with a tree topology. We represent finite motion using Clifford algebra and infinitesimal motion using Lie algebra to perform finite dimensional kinematic synthesis of the multi-fingered mechanism. This allows tasks to be defined not only by displacements, but also by the velocity and acceleration at different positions for the design of robotic hands. The additional information enables an increased local approximation of the task at critical positions, as well as contact and curvature specifications. An example task is provided using an experimental motion capture system and we present the design of a robotic hand for the task using a hybrid Genetic Algorithm/Levenberg-Marquadt solver.

Links: Speaker Info, ARK-12

175 Seminar 29-5-2012

Speaker: Josep M. Olm ()
Title: Model-reference adaptive control with minimal controller synthesis
Abstract:

In this talk we will introduce a class of passivity-based MRAC controllers that require Minimal Controller Synthesis (MCS). This means that, for systems in control canonical form with unknown (possibly time-varying) parameters and subject to matched, unmodelled nonlinearities and/or disturbances, the MCS strategy is able to ensure tracking of the states of a given reference model. We will also comment on the extensions of the MCS algorithm to discrete-time systems and its effectiveness in the implementation of MCS controllers for continuous-time plants. The proposal will be illustrated with numerical and experimental results as well.

Links: Speaker Info

174 Seminar 24-5-2012

Speaker: Eduard Serradell ()
Title: Robust Non-Rigid Registration of 2D and 3D Graphs
Abstract:

We present a new approach to matching graphs embedded in R2 or R3. Unlike earlier methods, our approach does not rely on the similarity of local appearance features, does not require an initial alignment, can handle partial matches, and can cope with non-linear deformations and topological differences. To handle arbitrary non-linear deformations, we represent them as Gaussian Processes. In the absence of appearance information, we iteratively establish correspondences between graph nodes, update the structure accordingly, and use the current mapping estimate to find the most likely correspondences that will be used in the next iteration. This makes the computation tractable. We demonstrate the effectiveness of our approach first on synthetic cases and then on angiography data, retinal fundus images, and microscopy image stacks acquired at very different resolutions.

Links: Speaker Info, CVPR-12

173 Seminar 15-5-2012

Speaker: Júlia Borràs ()
Title: Static analysis of parallel manipulators with compliant joints
Abstract:

Many robotic hands use compliant joints because they provide several advantages when interacting with objects in unknown environments, but they also modify the relation between external and internal forces and vary the reachable workspace. We propose a detailed study of how compliant joints modify the statics of hands, from the point of view of parallel manipulators. Starting with fully actuated parallel manipulators, the chosen mathematical framework clarifies the role of joint compliance and allows to measure the reduction/increase of torque exerted by the active joints due to the influence of the passive compliant ones.

Links: Speaker Info

172 Seminar 10-5-2012

Speaker: Oriol Bohigas ()
Title: A Singularity-free Path Planner for Closed-chain Manipulators
Abstract:

This paper provides an algorithm for computing singularity-free paths on non-redundant closed-chain manipulators. Given two non-singular configurations of the manipulator, the method attempts to connect them through a configuration space path that maintains a minimum clearance with respect to the singularity locus at all points. The method is resolution complete, in the sense that it always returns a path if one exists at a given resolution, or returns failure otherwise. The path is computed by defining a new manifold that maintains a one-to-one correspondence with the singularity-free configuration space of the manipulator, and then using a higher-dimensional continuation technique to explore this manifold systematically from one configuration, until the second configuration is found. Examples are included that demonstrate the performance of the method on illustrative situations.

Links: Speaker Info, ICRA-12

Seminar 26-4-2012

171 Speaker: Arnau Ramisa ()
Title: Using Depth and Appearance Features for Informed Robot Grasping of Highly Wrinkled Clothes
Abstract:

Detecting grasping points is a key problem in cloth manipulation. Most current approaches follow a multiple re-grasp strategy for this purpose, in which clothes are sequentially grasped from different points until one of them yields to a desired configuration. In this paper, by contrast, we circumvent the need for multiple re-graspings by building a robust detector that identifies the grasping points, generally in one single step, even when clothes are highly wrinkled.
In order to handle the large variability a deformed cloth may have, we build a Bag of Features based detector that combines appearance and 3D geometry features. An image is scanned using a sliding window with a linear classifier, and the candidate windows are refined using a non-linear SVM and a grasp goodness criterion to select the best grasping point. We demonstrate our approach detecting collars in deformed polo shirts, using a Kinect camera. Experimental results show a good performance of the proposed method not only in identifying the same trained textile object part under severe deformations and occlusions, but also the corresponding part in other clothes, exhibiting a degree of generalization.

Links: Speaker Info, ICRA-12

170 Speaker: Carlos Rosales ()
Title: On the Synthesis of Feasible and Prehensile Robotic Grasps
Abstract:

This work proposes a solution to the grasp synthesis problem, which consist of finding the best hand configuration to grasp a given object for a specific manipulation task while satisfying all the necessary constraints. This problem had been divided into sequential sub-problems, including contact region determination, hand inverse kinematics and force distribution, with the particular constraints of each step tackled independently. This may lead to unnecessary effort, such as when one of the problems has no solution given the output of the previous step as input. To overcome this issue, we present a kinestatic formulation of the grasp synthesis problem that introduces compliance both at the joints and the contacts. This provides a proper framework to synthesize a feasible and prehensile grasp by considering simultaneously the necessary grasping constraints, including contact reachability, object restraint, and force controllability. As a consequence, a solution of the proposed model results in a set of hand configurations that allows to execute the grasp using only a position controller. The approach is illustrated with experiments on a simple planar hand using two fingers and an anthropomorphic robotic hand using three fingers.

Links: Speaker Info, ICRA-12

169 Seminar 30-3-2012

Speaker: Elisa Martinez ()
Title: Expressive Deformation Profile for Face Recognition
Abstract:

Face recognition has been a productive field of research for many years. Most of the existing solutions, however, are based on still images and only reach their best performance when the subjects want to be recognized. As face recognition systems continue to improve, better performance is demanded in extreme conditions or challenging situations, especially by the security industry, interested in more reliable biometric systems. Motion-based face recognition is a young research area which is motivated by this growing demand and is inspired by psychological studies that highlight the importance of motion when humans perform facial recognition. The talk will present a novel motion-based face recognition approach, the Expressive Deformation Profile, on which I've worked in collaboration with the Computer Vision Lab at the National University of Singapore. Results will be shown from experiments in a general Facial Expression Database (Cohn-Kanade) and in a twins' facial expression database to test the extreme challenge of distinguishing among identical-twins.

Links: Speaker Info

168 Seminar 15-3-2012

Speaker: Joost van de Weijer ()
Title: Intrinsic Images for Color Image Understanding
Abstract:

In this talk I will advocate the usage of the dichromatic reflection model for color image understanding. This model allows us to explain images by means of their intrinsic properties, and it allows us to discard scene incidental events such as specularities and shadows. The model separates the surface reflectance in Lambertian reflectance and specular reflectance.
I will apply the model to a variety of computer vision and image processing problems. The main aim of this talk is to show that when working with RGB values it is often beneficial to think in terms of the dichromatic reflection model. The model provides the link between variations of RGB values and physical events in the world. To demonstrate this, I will apply the model to derive photometric invariant differential structure of images. Next, I will use the model to estimate the illuminant in a scene, allowing for color constancy. The aim of the talk is to reveal the strength of the dichromatic model in color image understanding. Finally, I will conclude with several extensions of the model which include modeling multiple illuminants and ambient lighting.

Links: Speaker Info

167 Seminar 1-3-2012

Speaker: Ruben Martinez-Cantin ()
Title: The non-convex paradigm for learning and experimental design
Abstract:

Traditionally, the fields of learning and experimental design have been characterized for relying in convex functions, which allow to solve high dimensional problems efficiently. However, many scientific and engineering problems cannot be directly formulated in terms of convex functions. Classical approaches in learning and decision making are based on strong assumptions and approximations such as discretization of the search space or the selection of suboptimal (local) decisions. In this talk, I'm going to present some recent improvements in the fields of non-convex optimization, Bayesian optimization and non-parametric bandits which can be used as an efficient alternative to the convex/approximate solution. The talk includes a couple of robotics problems where these techniques have shown their potential. Finally, I'm going to present a free toolbox that we have recently developed with all these methods.

Links: Speaker Info

166 Seminar 21-12-2011

Speaker: Ariadna Quattoni ()
Title: Spectral Methods for Learning Finite State Transducers with applications to Sequence Prediction
Abstract:

The problem of modeling paired input-output sequences arises in numerous application areas such as natural language processing, speech recognition and biology. One popular tool for modeling paired input-output sequences is to use Probabilistic Finite-State Transducers (FSTs). Because FSTs induce a latent state representation they can be very effective in modeling the intrinsic dynamics of input-output sequences. My recent work has focused on studying how FSTs can be used to solve real sequence prediction tasks. This involves developing efficient learning algorithms for inducing FSTs from supervised data.
Most algorithms for FST learning rely on gradient-based or EM optimizations which can be computationally expensive and suffer from local optima issues. In the first part of the talk, I will follow a different approach and present a spectral algorithm for FST learning with strong PAC-style guarantees. At its core, the algorithm is simple and scalable to large data sets. Our approach follows a both recent and no-so-recent line of work which is based on using multiplicity automaton representations of finite state machines. In the second part of the talk, I will discuss ongoing work on using this algorithm for sequence prediction problems in natural language processing and highlight possible uses of FST learning in other application areas.

Links: Speaker Info

165 Seminar 14-12-2011

Speaker: Adrià Colomé ()
Title: Inverse Kinematics Algorithms for serial Redundant Manipulators.
Abstract:

The inverse kinematics (IK) of a robot is the mapping that, given a goal position, calculates a set of joint positions so as to place the robot's end effector in the specified goal. As this is one of the most relevant computations to move the robot, there has been a lot of work about obtaining a fast and robust IK algorithm. In this seminar we will explain the difficulties on analytically obtaining the inverse kinematics of a robot, and we present the most used closed-loop methods, pointing out their main concerns, numerical error, convergence, capability of reaching secondary goals, and respecting joint limits. We also present two enhancements of some of the methods exposed. One of them, by filtering the Jacobian matrix eigenvalues before calculating its pseudoinverse. The other, as a combination of other existing methods.

Links: Speaker Info

164 Seminar 1-12-2011

Speaker: Leonel Rozo ()
Title: Robot learning from demonstration of force-based tasks with multiple solution trajectories
Abstract:

A learning framework with a bidirectional communication channel is proposed, where a human performs several demonstrations of a task using a haptic device (providing him/her with force-torque feedback) while a robot captures these executions using only its force-based perceptive system. Our work departs from the usual approaches to learning by demonstration in that the robot has to execute the task blindly, relying only on force-torque perceptions, and, more essential, we address goal-driven manipulation tasks with multiple solution trajectories, whereas most works tackle tasks that can be learned by just finding a generalization at the trajectory level. To cope with these multiple-solution tasks, in our framework demonstrations are represented by means of a Hidden Markov Model (HMM) and the robot reproduction of the task is performed using a modified version of Gaussian Mixture Regression that incorporates temporal information (GMRa) through the forward variable of the HMM. Also, we exploit the haptic device as a teaching and communication tool in a human-robot interaction context, as an alternative to kinesthetic-based teaching systems. Results show that the robot is able to learn a container-emptying task relying only on force-based perceptions and to achieve the goal from several non-trained initial conditions.

Links: Speaker Info

163 Seminar 10-10-2011

Speaker: Hiroshi Ishiguro ()
Title: Humanlike information media for bridging people
Abstract:

Not provided by the speaker.

Links: Speaker Info

162 Seminar 29-09-2011

Speaker: Oscar Martinez Mozos ()
Title: Robotic perception in indoor environments
Abstract:

Whenever service robots have to interact with humans they need to perceive the environment in a similar way people do. This talk presents several works related to robotic perception in indoor environments. It first introduces the categorization of typical places found indoors, like for example corridors and offices, using different types of sensors. In addition, it presents some techniques to detect other agents and objects such as people or typical pieces of furniture. Finally, it describes a conceptual representation of an indoor environment that can be created by a service robot by integrating the previous approaches.

Links: Speaker Info

161 Seminar 20-9-2011

Speaker: Léonard Jaillet ()
Title: EG-RRT: Environment-Guided Random Trees for Kinodynamic Motion Planning with Uncertainty and Obstacles
Abstract:

Existing sampling-based robot motion planning methods are often inefficient at finding trajectories for kino- dynamic systems, especially in the presence of narrow passages between obstacles and uncertainty in control and sensing. To address this, we propose EG-RRT, an Environment-Guided variant of RRT designed for kinodynamic robot systems that combines elements from several prior approaches and may incorporate a cost model based on the LQG-MP framework to estimate the probability of collision under uncertainty in control and sensing. We compare the performance of EG-RRT with several prior approaches on challenging sample problems. Results suggest that EG-RRT offers significant improvements in performance.

Links: Speaker Info, IROS 2011

160 Seminar 28-07-2011

Speaker: Léonard Jaillet ()
Title: Path Planning with Loop Closure Constraints using an Atlas-based RRT
Abstract:

In many relevant path planning problems, loop closure constraints reduce the configuration space to a manifold embedded in the higher-dimensional joint ambient space. Whereas many progresses have been done to solve path planning problems in the presence of obstacles, only few work consider loop closure constraints. In this paper, we present the AtlasRRT algorithm, a planner specially tailored for such constrained systems that builds on recently developed tools for higher-dimensional continuation. These tools provide procedures to define charts that locally parametrize manifolds and to coordinate them forming an atlas. AtlasRRT simultaneously builds an atlas and a Rapidly-Exploring Random Tree (RRT), using the atlas to sample relevant configurations for the RRT, and the RRT to devise directions of expansion for the atlas. The advantage of the new planner comes from that samples obtained from the atlas allow a more efficient extension of the RRT as compared to state of the art approaches where samples are generated in the joint ambient space.

Links: Speaker Info, ISRR 2011

159 Seminar 21-07-2011

Speaker: Nicolás Rojas ()
Title: A Coordinate-Free Approach to Tracing the Coupler Curves of Pin-Jointed Linkages
Abstract:

In general, high-order coupler curves of plane mechanisms cannot be properly traced by standard predictor-corrector algorithms due to drifting problems and the presence of singularities. Instead of focusing on finding better algorithms for tracing curves, a simple coordinate-free method that first traces these curves in a distance space and then maps them onto the mechanism workspace is proposed. Tracing a coupler curve in the proposed distance space is much simpler because (a) the equation of this curve in this space can be straightforwardly obtained from a sequence of bilaterations; and (b) the curve in this space naturally decomposes into branches in which the signs of the oriented areas of the triangles involved in the aforementioned bilaterations remain constant. A surjective mapping permits to map the thus traced curves onto the workspace of the mechanism. The advantages of this two-step method are exemplified by tracing the coupler curves of a double butterfly linkage, curves that can reach order 48.

Links: Speaker Info, IDETC-MECH 2011

158 Seminar 18-07-2011

Speaker: Reid Simmons ()
Title: Human-Robot Social Interaction
Abstract:

Dr. Reis Simmons is a professor at de Carnigie Mellon University, his research interests focus on developing reliable, highly autonomous systems (especially mobile robots) that operate in rich, uncertain environments. The goal is to create intelligent systems that can operate autonomously for long periods of time in unstructured, natural environments. This necessitates robots that can plan, effectively reason about uncertainty, diagnose and recover from unanticipated errors, and reason about their limitations. In particular, he is interested in architectures for autonomy that combine deliberative and reactive behavior, reliable execution monitoring and error recovery, multi-robot coordination, probabilistic and symbolic planning, formal verification of autonomous systems, and human-robot social interaction.

Links: Speaker Info

157 Seminar 14-07-2011

Speaker: Reinaldo Bianchi ()
Title: Accelerating Reinforcement Learning by using heuristic selection of actions
Abstract:

This seminar will present how to make improved action selection for online policy learning in robotic scenarios using reinforcement learning (RL) algorithms. Since finding control policies using any RL algorithm can be very time consuming, we combine RL algorithms with heuristic functions for selecting promising actions during the learning process. Several methods have been successfully applied for defining the heuristic function, including defining a heuristics using information from the learning process, the use of previous domain knowledge in fixed heuristics and the reuse of previously learned policies, in a Case-Based Reasoning approach. Experimental results on robot navigation will show that the use of very simple heuristic functions results in significant performance enhancement of the learning rate.

Links: Speaker Info

156 Seminar 13-07-2011

Speaker: Fabio Ramos ()
Title: Continuous Occupancy Mapping with Gaussian Processes
Abstract:

Since it was proposed in 1989, occupancy grids have been a very popular technique to represent the environment. Despite its efficiency, occupancy grids have a number of drawbacks - it requires an arbitrary discretisation of the space, it makes strong assumptions of independence between cells, and it does not scale well to 3D, typically requiring external data structures. In this talk I will introduce a novel methodology to continuous occupancy maps directly addressing these issues, the Gaussian Process Occupancy Maps (GPOM). The problem of occupancy mapping is tackled as a classification task where the robot's environment is classified into regions of occupancy and free space. This is obtained by employing a modified Gaussian process as a non-parametric Bayesian learning technique to exploit the fact that real-world environments inherently possess structure. The result is an anytime algorithm capable of generating accurate representations of large environments at arbitrary resolutions to suit many applications. I will show how to extend GPOM to account for the vehicle's uncertainty in position, and efficient strategies to handle 3D representations.

Links: Speaker Info

155 Seminar 7-07-2011

Speaker: Anaís Garrell ()
Title: Human-Robot Dialogues in the Scrabble Game
Abstract:

Nowadays, research opens new fields in the development of entertainment robots, those robots are able to interact with people and share their recreation time. Following this idea, Dr. Reid Simmons, Professor of the Robotics Institute at the Carnigie Mellon University (CMU), began a project in which a robot plays the scrabble game with 1, 2 or 3 people. Besides, the robot is able to interact with people through dialogue and facial expressions. In consequence, this seminar will be focused on the dialogue of the robot to create an engagement with people and start the game, furthermore, it will be shown how should be the conversation during the game depending on players' behavior.

Links: Speaker Info

154 Seminar 29-06-2011 (12 hs.)

Speaker: Ricardo Sánchez-Peña ()
Title: Identificación y Control: Inválidos, falsos y corruptos
Abstract:

Se desarrollarán temas de fondo en el área de la Identificación y Control de sistemas dinámicos. Se expondrán los fundamentos de la Identificación Robusta y de la Falsificación de Controladores surgidas a principios y finales de los '90, respectivamente. Se tratará especialmente el manejo de la información relativa a la determinación de familias de modelos, la invalidación de los mismos, los niveles de conservadurismo (u optimismo) y su uso en el control de sistemas físicos. Se ejemplificará con casos de aplicación en los que intervino el autor.

Links: ITBA

153 Seminar 16-06-2011

Speaker: Carlos Rosales ()
Title: Global Optimization of Robotic Grasps
Abstract:

This talk presents a procedure to optimize the quality of robotic grasps for objects that need to be held and manipulated in a specific way, characterized by a number of tight contact constraints. The main difficulties of the problem include that the set of feasible grasps is a manifold implicitly defined by a system of non-linear equations, the high dimension of this manifold, and the multi-modal nature of typical grasp quality indices, which make local optimization methods get trapped into local extrema. The proposed procedure finds a way around these difficulties by focussing the exploration on a relevant subset of grasps of lower dimension, which is traced out exhaustively using higher-dimensional continuation techniques. Using these techniques, a detailed atlas of the subset is obtained, on which the highest quality grasp according to any desired criterion can be readily identified. We will show results on a 3-finger planar hand and on the Schunk anthropomorphic hand to validate the approach.

Links: Speaker Info, RSS

152 Seminar 6-06-2011

Speaker: Michael Villamizar ()
Title: Detection Performance Evaluation of Boosted Random Ferns
Abstract:

We present an experimental evaluation of Boosted Random Ferns in terms of the detection performance and the training data. We show that adding an iterative bootstrapping phase during the learning of the object classifier, it increases its detection rates given that additional positive and negative samples are collected (bootstrapped) for retraining the boosted classifier. After each bootstrapping iteration, the learning algorithm is concentrated on computing more discriminative and robust features (Random Ferns), since the bootstrapped samples extend the training data with more difficult images. The resulting classifier has been validated in two different object datasets, yielding successful detections rates in spite of challenging image conditions such as lighting changes, mild occlusions and cluttered background.

Links: Speaker Info, IBPRIA-11

151 Seminar 01-06-2011 (16:00 hs, IRI's Seminar Room)

Speaker: Romeo Ortega ()
Title: Some Control Theory Problems in Modern Energy Systems
Abstract:

In this talk we present some recent theoretical developments concerning the following topics related with energy applications:

- A dynamic router for energy management applications.

- Transient stability of power systems revisited.

- A wind speed estimator for windmill systems.

Links: Speaker Info, Supelec

150 Seminar 30-05-2011

Speaker: Frank Dellaert ()
Title: The Bayes Tree and Inference in Large-Scale Graphical Models for SLAM and SFM
Abstract:

Simultaneous Localization and Mapping (SLAM) and Structure from Motion (SFM) are important and closely related problems in robotics and vision. Not surprisingly, there is a large literature describing solutions to each problem, and more and more connections are established between the two fields. At the same time, robotics and vision researchers alike are becoming increasingly familiar with the power of graphical models as a language in which to represent inference problems. In this talk I will show how SLAM and SFM can be posed in terms of factor graphs, and how inference in them can be explained in a purely graphical manner via the concept of variable elimination. I will also show that, when applied to Gaussian problems, the algorithm yields the familiar QR and Cholesky factorization algorithms, and that this connection with linear algebra leads to strategies for very fast inference in arbitrary graphs. I will then present the Bayes tree as a novel data structure for representing the inferred posteriors. The Bayes tree is similar to a junction tree, yet better embodies the connection with sparse linear algebra. I will conclude by showing some published and preliminary work that exploits this connection to the fullest.

Links: Speaker Info

149 Seminar 27-05-2011

Speaker: Oscar Sandoval ()
Title: Distributed dynamic task allocation, an approach for time dependent tasks
Abstract:

In last years many research work has focused in different areas within multi-robot systems. One of these areas is multi-robot task allocation, which involves the distribution of tasks between robots. Despite the large number of proposals in literature, there are already many open issues inside multi-robot task allocation, for example task reallocation.
Task reallocation or dynamic task allocation, implies the capacity of the system to change previously done assignations (links between robots and tasks) to get "best" system performance.
In this paper we present a distributed task reallocation approach, that relies on an energy-based formalization which considers tasks that change over time. We use simulation to show the performance of this proposal and to compare with other architectures.

Links: Speaker Info

148 Seminar 12-05-2011

Speaker: Edgar Simo ()
Title: Kinematic Model of the Hand using Computer Vision
Abstract:

The project developed uses a single camera to capture images of a moving human hand. The position and orientation of the finger nails is estimated throughout time, using computer vision. A dimensional kinematic synthesis solver is used in order to calculate the joint axes and the rotations of around each joint for a task. This allows creating personalized kinematic models of human hands for use in both prosthesis and exoskeleton designs.

Links: Speaker Info

Seminar 5-05-2011

147 Speaker: Guillem Alenyà ()
Title: 3D modelling of leaves from color and ToF data for robotized plant measuring
Abstract:

Supervision of long-lasting extensive botanic experiments is a promising robotic application that some recent technological advances have made feasible. Plant modelling for this application has strong demands, particularly in what concerns 3D information gathering and speed. This paper shows that Time-of-Flight (ToF) cameras achieve a good compromise between both demands, providing a suitable complement to color vision. A new method is proposed to segment plant images into their composite surface patches by combining hierarchical color segmentation with quadratic surface fitting using ToF depth data. Experimentation shows that the interpolated depth maps derived from the obtained surfaces fit well the original scenes. Moreover, candidate leaves to be approached by a measuring instrument are ranked, and then robot-mounted cameras move closer to them to validate their suitability to being sampled. Some ambiguities arising from leaves overlap or occlusions are cleared up in this way. The work is a proof-of-concept that dense color data combined with sparse depth as provided by a ToF camera yields a good enough 3D approximation for automated plant measuring at the high throughput imposed by the application.

Links: Speaker Info, ICRA-11

146 Speaker: Rafael Valencia ()
Title: Path Planning in Belief Space with Pose SLAM
Abstract:

The probabilistic belief networks that result from standard feature-based simultaneous localization and map building cannot be directly used to plan trajectories. The reason is that they produce a sparse graph of landmark estimates and their probabilistic relations, which is of little value to find collision free paths for navigation. In contrast, we argue in this paper that Pose SLAM graphs can be directly used as belief roadmaps. We present a method that devises optimal navigation strategies by searching for the path in the pose graph with lowest accumulated robot pose uncertainty, independently of the map reference frame. The method shows improved navigation results when compared to shortest paths both over synthetic data and real datasets.

Links: Speaker Info, ICRA-11

145 Seminar 04-05-2011

Speaker: Alicia Arce ()
Title: On the Exploitation of MPC Formulations for Performance Enhancement of Fuel Cell Systems
Abstract:

In this talk, several control problems for fuel cell systems will be studied; specifically I will focus on the temperature and air supply control for stand-alone fuel cell systems and on the power management for fuel cell hybrid vehicles. For both cases, advanced MPC formulations (hybrid and explicit formulations) will be proposed to deal with piece-wise affine models, binary variables and short sampling times. The advantages of this technology and real-time implementation feasibility will be discussed with experimental data.

Links: Speaker Info, IDENER

144 Seminar 7-04-2011

Speaker: Nicolás Rojas ()
Title: On Baranov Trusses and Modular Kinematic Analysis
Abstract:

Nowadays, the solution of the position analysis problem of a planar linkage almost invariably implies, as a first step, obtaining a system of equations from the independent kinematic loops of the mechanism, since general methods for the analysis of any planar linkage has been developed based on this set of equations in the last 10-15 years. As a consequence, the use of this approach, the so-called loop closure equations method, has been so overwhelming that other approaches for deriving constraints of the valid configurations of a linkage seem to have been banished from literature. However, this standard approach is beginning to be questioned because even for simple linkages the resulting system of kinematic equations is so involved that its solution requires the use of complex variable elimination techniques or special numerical algorithms. In this seminar, the modular kinematic analysis of planar linkages using Baranov trusses is presented as an alternative to the loop closure equations method. Basic definitions, advantages, drawbacks and examples of this approach are discussed.

Links: Speaker Info

143 Seminar 05-04-2011 (17 hs.). IRI's Main Meeting Room

Speaker: Naira Hovakimyan (Illinois University) ()
Title: L1 Adaptive Control
Abstract:

The history of adaptive control systems dates back to early 50-s, when the aeronautical community was struggling to advance aircraft speeds to higher Mach numbers. In November of 1967, X-15 launched on what was planned to be a routine research flight to evaluate a boost guidance system, but it went into a spin and eventually broke up at 65,000 feet, killing the pilot Michael Adams. It was later found that the onboard adaptive control system was to be blamed for this incident. Exactly thirty years later, fueled by advances in the theory of nonlinear control, Air Force successfully flight tested the unmanned unstable tailless X-36 aircraft with an onboard adaptive flight control system. This was a landmark achievement that dispelled some of the misgivings that had arisen from the X-15 crash in 1967. Since then, numerous flight tests of Joint Direct Attack Munitions (JDAM) weapon retrofitted with adaptive element have met with great success and have proven the benefits of the adaptation in the presence of component failures and aerodynamic uncertainties. However, the major challenge related to stability/robustness assessment of adaptive systems is still being resolved based on testing the closed-loop system for all possible variations of uncertainties in Monte Carlo simulations, the cost of which increases with the growing complexity of the systems. This talk will give an overview of the limitations inherent to the conventional adaptive controllers and will introduce the audience to the L1 adaptive control theory, the architectures of which have guaranteed robustness in the presence of fast adaptation. Various applications, including flight tests of a subscale commercial jet, will be discussed during the presentation to demonstrate the tools and the concepts. With its key feature of decoupling adaptation from robustness L1 adaptive control theory has facilitated new developments in the areas of event-driven adaptation and networked control systems. A brief overview of initial results and potential directions will conclude the presentation.

Links: Speaker Info

142 Seminar 31-03-2011

Speaker: Iván Huerta ()
Title: Foreground Object Segmentation and Shadow Detection for Video Sequences in Uncontrolled Environments
Abstract:

This talk is mainly divided in two parts. The first one presents a study of motion segmentation problems. Based on this study, a novel algorithm for mobile-object segmentation from a static background scene is also presented. This approach is demonstrated robust and accurate under most of the common problems in motion segmentation. The second one tackles the problem of shadows in depth. Firstly, a bottom-up approach based on a chromatic shadow detector is presented to deal with umbra shadows. Secondly, a top-down approach based on a tracking system has been developed in order to enhance the chromatic shadow detection.
In our first contribution, a case analysis of motion segmentation problems is presented by taking into account the problems associated with different cues, namely colour, edge and intensity. Our second contribution is a hybrid architecture which handles the main problems in the case analysis, by fusing (i) these three cues and (ii) a temporal difference algorithm. On the one hand, we enhance the colour and edge models to solve both global/local illumination changes (shadows and highlights) and camouflage in intensity. In addition, local information is exploited to cope with camouflage in chroma. On the other hand, the intensity cue is also applied when colour and edge cues are not available, such as when beyond the dynamic range. Additionally, temporal difference is included to segment motion when these three cues are not available. Lastly, the approach is enhanced for allowing ghost detection.
Most segmentation approaches dealing with shadow detection are typically restricted to penumbra shadows. Therefore, such techniques cannot cope well with umbra shadows.
In the Bottom-up part, the shadow detection approach applies a novel technique based on gradient and colour models for separating chromatic moving shadows from moving objects. Hereafter, the regions corresponding to potential shadows are grouped by considering a bluish effect and an edge partitioning. Lastly, (i) temporal similarities between local gradient structures and (ii) spatial similarities between chrominance angle and brightness distortions are analysed for all potential shadow regions in order to finally identify umbra shadows. In the top-down process, after detection of objects and shadows both are tracked using Kalman filters. Firstly, this implies a data association and a case analysis between the blobs (foreground and shadow) and Kalman filters. Based on this association, temporal consistency is looked for the association between foregrounds and shadows and their respective Kalman Filters. From this association several cases are studied, as a result lost chromatic shadows are correctly detected. As a result, our approach obtains very accurate and robust motion segmentation in both indoor and outdoor scenarios, as quantitatively and qualitatively demonstrated in the experimental results, by comparing our approach with most best-known state-of-the-art approaches.

Links: Speaker Info

141 Seminar 31-03-2011 (10 hs.). IOC's Seminars Room

Speaker: Martin Cech ()
Title: Practical robust controller design for fractional systems
Abstract:

The outline for this presentation is as follows:

- basic theory of fractional systems in time and frequency domain

- using fractional systems for process identification (model set approach)

- robustness regions method for PID and fractional PID controller design

- advantages and disadvantages of fractional PID controller

- realization of simple fractional elements

- industrial applications

- interactive Java applets More info

Links: Speaker Institution

140 Seminar 28-03-2011 (Cancelled by the speaker)

Speaker: Leonel Rozo ()
Title: A Robot Learning from Demonstration Framework to Teach Force-based Multi-Trajectory Tasks
Abstract:

Robot learning from demonstration stands out as a promising way to endow robots with learning capabilities and allow human beings to teach them tasks in a natural way. This talk addresses how a robot can learn a multi-trajectory task based on its force/torque perceptions while a human teacher demonstrates the task, and how he/she can be more involved in the learning process by feeling through haptic feedback what the robot senses during the demonstration phase. Here, a learning framework with a bidirectional communication channel is proposed, where a human teacher teleoperates a robotic arm using a haptic interface. The teacher demonstrates a container-emptying task while receiving F/T feedback sensed by a force sensor placed on the robotic wrist. Teacher demonstrations are analyzed under the Mutual Information criterion for extracting relevant input variables for the task. Resulting data streams are statistically encoded using a Hidden Markov Model (HMM) and the execution phase is developed by implementing a modified version of Gaussian Mixture Regression that uses implicit temporal information from the HMM. Results show how the robot is able to learn a multi-trajectory task using only force-based perceptions, which can be exploited in further settings where other perception systems do not provide enough information about a specific task to be learned or scenarios where force-based data provide a better understanding about what the robot has to learn.

Links: Speaker Info

139 Seminar 3-03-2011

Speaker: Arnau Ramisa ()
Title: Combining attributes and Fisher vectors for efficient image retrieval
Abstract:

Attributes have recently shown to give excellent results for category recognition. In this paper, we demonstrate their performance in the context of image retrieval. First, we show that retrieval based on attribute vectors gives results comparable to the state of the art if retrieving images of a particular object. Second, we demonstrate that combining attribute and Fisher vectors improves performance for retrieval of the same object as well as categories. Third, we implement an efficient coding technique for compressing the combined descriptor to very small codes. Experimental results on the Holidays dataset show that our approach significantly outperforms the state-of-the-art, even for a very compact representation of 16 bytes per image. Retrieving category images is evaluated on a web dataset set with text annotation for learning representations. We present a baseline for retrieval based on images only and show the contribution of attribute features. Furthermore, we show how the combined image features can supplement text features.

Links: Speaker Info

138 Seminar 02-03-2011, 16 hs. (Sala de Juntas de FME)

Speaker: Dra. Lucia Marucci ()
Title: Mathematical analysis, modelling and design of synthetic biological circuits
Abstract:

The functioning and development of living organisms is controlled on the molecular level by networks of genes, proteins, small molecules, and by their mutual interactions. Prediction, control, and understanding of these systems, named gene regulatory networks, arise mainly from modelling them using iterated computer simulations and non-linear mathematical analysis. Biotechnological advances in quantitative high-throughput technology, in combination with the growing inter-disciplinarity between biology with engineering and natural sciences, have made this challenge achievable thanks to the emerging fields of Systems and Synthetic biology. In the seminar we will first present an introduction to the mentioned disciplines, and to the mathematical tools commonly used. Secondly, we will provide, as example, results about the mathematical modelling and non-linear analysis of IRMA, a novel synthetic network built in the yeast for benchmarking different computational methods.

Links: Info about CRG

137 Seminar 17-02-2011

Speaker: Diego Pardo ()
Title: Learning Robot Motor Skills
Abstract:

The types of tasks that a manipulator can complete have been taken one step further using machine learning (ML) approaches. Tasks where the robot interacts with very complex dynamics have been used to demonstrate the benefits of ML in the exploitation of manipulator capacities. This seminar will review some state-of-the-art techniques in ML for the generation of robotic motions, including approaches based on learning by demonstration and reinforcement learning in continuous states and action spaces.

Links: Speaker Info

136 Seminar 01-02-2011 (12 hs. at the IOC Conference Room)

Speaker: Bernat Joseph i Duran ()
Title: A simple model for sewage networks: an MPC approach
Abstract:

It will be described the modeling and model predictive control of sewage water networks in presence of episodes of heavy rain. The results are based on a new branching algorithm that has been developed and implemented in the last months in Heidelberg University to solve the associated optimization problem.

Links: ACES Group

135 Seminar 27-1-2011

Speaker: Waldir L. Roque ()
Title: Path Planning with Generalized Voronoi Diagrams
Abstract:

Generalized Voronoi Diagram (GVD) is an important geometrical technique with several applications in different fields. In this talk it will be discussed an application of GVD to autonomous robot path planning with a global vision system. The GVD technique was implemented and some experiments were conducted with Khepera Lab Robots.

Links: Speaker info

134 Seminar 20-1-2011

Speaker: Albert Ambrós ()
Title: Disseny, Implementació i Calibratge d'una Plataforma de Gough-Stewart
Abstract:

El present projecte és un estudi sobre un aspecte important en el disseny d'un robot paral.lel. En particular, l'objectiu és desenvolupar i aplicar una metodologia per a trobar les posicions d'uns punts característics d'una plataforma de Gough-Stewart reconfigurable. Específicament, els punts a trobar del robot són els punts d'ancoratge de les cadenes cinemátiques tant a la base com a la plataforma, que fa d'efector final. Un objectiu secundari és determinar si és factible l'ús de servo motors de mida i cost reduïts per a la fabricació de robots paral.lels. Per aconseguir aixó s'ha desenvolupat un métode numéric i s'ha construït un prototipus sobre el que s'ha provat aquest métode. Degut a la complexitat de definir matemàticament la naturalesa dels robots paral.lels i al seu elevat nombre d'incògnites, el cálcul utilitzat és un algorisme iteratiu basat en el métode de Newton. Els resultats d'aplicar aquesta metodologia són prou positius. No obstant, Les posicions trobades són tan properes com ho permet la precisió dels seus elements, ja sigui la del sensor com la dels propis motors.

Links:

133 Seminar 16-12-2010

Speaker: Adrian Peñate ()
Title: Uncalibrated pose estimation from 2D-to-3D correspondences
Abstract:

Se presentará un nuevo método no-iterativo para la obtención de la pose de la cámara y la distancia focal a partir de correspondencias 2D-3D. El método presentado ha demostrado ser robusto a ruido y a correspondencias erróneas. Se ha utilizado una aproximación similar a la utilizada en el algoritmo EPnP. Mostramos que las técnicas de linearización y relinearización utlizadas en el algoritmo EPnP presentan limitaciones que hemos solventado mediante lo que hemos definido como linearización y relinearización exhaustiva.
Presentaremos resultados sobre datos reales y sintáticos; a la vez, compararemos con métodos totalmente calibrados, el caso del EPnP, y con métodos como el DLT que necesitan encontrar la distancia focal. Estos resultados corroboran que el método presentado es capaz de ofrecer una precisión y robustez superior a otros métodos no calibrados y comparable a métodos calibrados.

Links: Speaker info

132 Seminar 2-12-2010

Speaker: Léonard Jaillet ()
Title: Path Planning on Manifolds using Randomized Higher-Dimensional Continuation
Time: 12:00
Abstract:

Despite the significant advances in path planning methods, problems in highly constrained spaces are still challenging. In particular, in many situations the configuration space is a non-parametrizable variety implicitly defined by constraints, which complicates the successful generalization of sampling-based path planners. In this paper, we present a new path planning algorithm specially tailored for highly constrained systems. It builds on recently developed tools for Higher-dimensional Continuation, which provide numerical procedures to describe an implicitly defined variety using a set of local charts. We propose to extend these methods to obtain an efficient path planner on varieties, handling highly constrained problems. The advantage of this planner comes from that it directly operates into the configuration space and not into the higher-dimensional ambient space, as most of the existing methods do.

Links: Speaker info, WAFR 2010

131 Seminar 2-12-2010

Speaker: Niliana Carrero ()
Title: Herramientas interactivas para control en modo de deslizamiento
Time: 11:00
Abstract:

En este trabajo se presentan herramientas interactivas en 2D y 3D para ilustrar los conceptos teóricos de control en modo de deslizamiento de sistemas dinámicos lineales a trozos. El objetivo final es permitir a los alumnos la posibilidad de visualizar la evolución del sistema y su respuesta en tiempo real, ante los cambios y configuraciones de toda una serie de parámetros del sistema en estudio. De esta manera, podrán obtener conocimientos prácticos y avanzar en la comprensión de la respuesta del sistema de manera cualitativa y cuantitativa. Las herramientas se han desarrollado mediante Sysqueke y Easy Java Simulations (EJS), que permite una fácil programación y diseño.

Links: ACES Group

130 Seminar 25-11-2010

Speaker: Montserrat Manubens ()
Title: Cusp points in the parameter space of degenerate 3-RPR planar parallel manipulators
Abstract:

In this seminar we are going to discuss the existence conditions of cuspidal configurations on some parameters of a special degenerate 3-RPR parallel manipulator. These configurations will be detected as cusp points, which make possible non-singular changes of assembly-mode and thus increase the singularity-free workspace.
We will combine algebraic-geometric techniques, such as Gröbner bases, discriminant varieties and cylindrical algebraic decomposition, in order to obtain a distribution of the number of cusp points. This distribution will let us analyze a whole family of degenerate 3-RPR manipulators that depend on one (or some) geometric parameter.
As a direct application of the presented results we obtain a simple method to determine the values of either the geometric parameters or the leg-rod lengths that make the manipulator be non-cuspidal.

Links: Speaker info

129 Seminar 11-11-2010

Speaker: Gerard Sanromà ()
Title: Robust Attributed Graph Matching with the EM Algorithm and the Graduated Assignment
Abstract:

The correspondence problem in computer vision is of basic importance since it arises at the early stages of many computer vision applications. So, it is of basic importance to develop efficient methods that are both robust -in the sense of being able to deal with noisy measurements- and, general -in the sense of having a wide field of application-. Attempts to solve the correspondence problem are based either on correlation or on feature descriptors. Since these methods use the local information around the interest points, that may produce erroneous matches under certain circumstances. Outlier rejectors such as RANSAC or Graph Transformation are used remove these erroneous matches by using global geometrical information. Since these outlier rejectors are unable to modify or produce new correspondences (different from those in the initial tentative-set), this may lead to poor results in the case of bad initial tentative-sets. Attributed graph matching methods are optimization techniques that exploit the nodes' attributes and their structural relations to compute new correspondences. We present an attributed graph matching approach that uses the geometrical information of the feature points in an affine-invariant way. It is also able to detect outlying features in both sides of the assignment, unlike other graph matching methods in the literature. We evaluate its performance in image matching and shape retrieval experiments.

Links:

128 Seminar 5-11-2010

Speaker: Viorela ila ()
Title: Methods for Large scale SLAM
Abstract:

This seminar introduces methods for Large scale SLAM developed during my stay at Georgia Tech. The motivation of this work comes from the fact that SLAM is a large optimization problem which requires very fast solvers when the result is used in real robotic applications. I will introduce two concepts; the Bayes tree and the subgraph preconditioning conjugate gradients (SPCG) and will conclude with an application to underwater 3D reconstruction of submerged structures.
The Bayes tree is a novel data structure that provides an algorithmic foundation enabling a better understanding of existing graphical model inference algorithms and their connection to sparse matrix factorization methods. Similar to a clique tree, a Bayes tree encodes a factored probability density, but unlike the clique tree it is directed and maps more naturally to the square root information matrix of the SLAM problem. The Bayes tree is used to obtain a completely novel algorithm for sparse nonlinear incremental optimization, that combines incremental updates with fluid relinearization of a reduced set of variables for efficiency. The new algorithm insures fast convergence to the exact solution.
SPCG is obtained by re-interpreting the method of conjugate gradients in terms of the graphical model representation of the SLAM problem. The main idea is to combine the advantages of direct and iterative methods, by identifying a sub-problem that can be easily solved using direct methods, and solving for the remaining part using PCG. The easy sub-problems correspond to a spanning tree, a planar subgraph, or any other substructure that can be efficiently solved. As such, our approach provides new insights into the performance of state of the art iterative SLAM methods based on re-parametrized stochastic gradient descent.

Links: Speaker info

127 Seminar 4-11-2010

Speaker: Jerónimo Moré ()
Title: Control design and development for a FC\SCs-based autonomous hybrid system
Place: IOC Conference Room (UPC)
Abstract:

The dynamic response of PEM fuel cells has a relatively slow component, mainly due to water and reactant gases dynamics. To overcome this limitation, FC-supercapacitors (SCs) topologies can be used in order to manage very fast power variations, presenting in addition high power density, long lifecycle and good charge/discharge efficiency. In this seminar, a FC\SC-based autonomous hybrid system for residential applications that is currently under development in the ACESs Fuel Cell Laboratory is presented. The FC and SCs are connected in parallel, through two separate DC/DC converters, to a DC bus. Under steady state conditions, the FC must deliver the load power requirement, while maintaining the SCs voltage regulated to the desired value. Under sudden load variations, the FC current rate must be limited to assure a safe transition to the new point of operation. During this current rate limitation mode, the SCs must deliver or absorb the power difference. To this end, a sliding mode strategy is proposed to satisfy to control objectives. The main one is the robust regulation of the DC bus voltage, even in the presence of system uncertainties and disturbances, such as load changes and FC voltage variations. Additionally, a second control objective is attained, namely to guarantee the adequate level of charge in the SCs, once the FC reaches the new steady state operation point. In this way, the system can meet the load power demand, even under sudden changes, and it can also satisfy a power demand higher than the nominal FC power, during short periods. The preliminary results presented, corresponds to the first stage of a joint project for the design and development of a novel FC\SCs-based autonomous hybrid system.

Links:

126 Seminar 28-10-2010

Speaker: Andreu Corominas ()
Title: Efficient Use of 3D Environment Models for Mobile Robot Simulation and Localization
Abstract:

This talk provides a detailed description of a set of algorithms to efficiently manipulate 3D geometric models to compute physical constraints and range observation models, data that is usually required in real-time mobile robotics or simulation. Our approach uses a standard file format to describe the environment and processes the model using the openGL library, a widely-used programming interface for 3D scene manipulation. The talk also presents results on a test model for benchmarking, and on a model of a real urban environment, where the algorithms have been effectively used for real-time localization in a large urban setting.

Links: Speaker info, SIMPAR 2010

125 Seminar 18-10-2010

Speaker: Beatriz Marcotegui ()
Title: Aplicaciones de la morfología matemática a la modelización urbana y a la localización de texto
Time: 17:00
Abstract:

El Centro de Morfología Matemática (CMM), fundador de la teoría que lleva su nombre, es un laboratorio de investigación en tratamiento de imagen de la Escuela de Minas de Paris (Mines ParisTech). Siempre motivado por las aplicaciones industriales, el CMM ha desarrollado herramientas muy eficaces de tratamiento de imagen como por ejemplo los filtros conexos, que eliminan el ruido de la imagen sin afectar los contornos de los objetos de interés o la segmentación jerárquica, que combinada con la teoría de grafos da lugar a algoritmos muy eficientes, tanto en tiempo de cálculo como en calidad de resultados. El CMM ha participado con éxito en muchos proyectos de investigación en diversos campos (biología, medicina, multimedia, materiales, ...). Un rápido panorama de los proyectos en curso actualmente será presentado. Se presentará con detalle los resultados de dos proyectos recientes:

  • TerraNumerica, en el que hemos desarrollado herramientas de análisis de escenas urbanas. El trabajo se centra en el análisis de nubes de puntos de escenas urbanas. El objetivo es extraer información semántica para una modelización realista. En primer lugar se segmenta la imagen en fachada, suelo u objeto presente en la escena. A continuación se clasifican los diferentes objetos de la escena (coche, peaton, farola...).

  • I-towns, en el que hemos puesto a punto un método de localización de texto en imágenes genéricas, que permite extraer información semántica de las imágenes para realizar búsquedas de alto nivel.

Links: Speaker info

124 Seminar 14-10-2010

Speaker: Júlia Borràs ()
Title: Singularity-Invariant Leg Substitutions in Pentapods
Abstract:

A pentapod is usually defined as a 5-degree-of-freedom fully-parallel manipulator with an axial spindle as moving platform. This kind of manipulators have revealed as an interesting alternative to serial robots handling axisymmetric tools. Their particular geometry permits that, in one tool axis, inclination angles of up to 90 degrees are possible thus overcoming the orientation limits of the classical Stewart platform. This talk presents a solution to the problem of finding those changes in the location of the leg attachments of a pentapod that leave its singularity locus invariant. Although the solution to this problem does not provide a fully characterization of the singularities, it provides a lot of insight into its nature. It is shown, for example, that there are four different architectures for a pentapod with a completely different behavior from the point of view of their singularities. The kinematics of pentaponds with coplanar attachments at the fixed base has previously been studied as rigid subassemblies of a Stewart platforms. In this talk, we treat the general case in which the base attachments are arbitrarily located in 3D space.

Links: Speaker info, IROS 2010

123 Seminar 7-10-2010

Speaker: Arnau Dória-Cerezo ()
Title: Port-Hamiltonian modeling of the Memristor and the higher order elements
Abstract:

Fourty years ago, Prof. L.O. Chua postulated the existence of a fourth element in the circuit theory; the Memristor. This element should complete, together with the conventional elements (resistors, inductances and capacitors), all the relationships between the electrical variables (currents, voltages, fluxes and charges). But it only existed on paper in the circuit theory... until 2008, when HP researchers found this missing element. This presentation is a big picture of the state of the art on the Memristor element. It starts with the definition and properties of the memristor and some possibles applications are pointed out. The universe of the two-terminal higher order elements is also introduced, and some of these elements are illustrated through easy examples. Finally, the port-Hamiltonian formalism is proposed to model these kind of elements. As conclusions, the open questions that arise during our research are presented.

Links: Speaker info

122 Seminar 13-9-2010

Speaker: Jerry Lin ()
Title: Development of Autonomous Robots and Robot Theater
Time: 11:00
Abstract:

This talk will cover the development of several autonomous robots with applications in education and entertainment and a unique robot theater project in the Advanced Intelligent Robotics Lab, Taiwan Tech (National Taiwan University of Science and Technology). The autonomous robots include desktop two-armed companion robots, DOC-1/DOC-2/DOC-3, which interactive functions in language teaching, board games, etc. The robot theater project developed four large sized humanoid robots, two two-wheeled, and two bipedal. The two-wheeled robots with two-arms can balance and move with two wheels, and can perform shows such as real-time portrait and music note reading-and-singing. The two bipedal robots are androids with two-arms and a human-like face capable of making facial expressions. They can walk and dance. The four robots together performed 5 shows to public in a robot theater in 27th December 2008. A new human-robot platform will be introduced at the end, which can significantly speed up the process and time for asking robots to operate on objects as a new assignment.

Links: Speaker info

121 Seminar 22-7-2010

Speaker: Dimitris Samaras ()
Title: Dense Non-rigid Surface Registration Using High-Order Graph Matching
Abstract:

In this talk I will give a brief overview of the activities in the Imaga Analysis Lab in Stony Brook University. I will focus on our work in 3D Facial Analysis and present our most recent techniques for non-rigid surface matching. I will describe a high-order graph matching formulation to address non-rigid surface matching. The singleton terms capture the geometric and appearance similar- ities (e.g., curvature and texture) while the high-order terms model the intrinsic embedding energy. The novelty of this paper includes: 1) casting 3D surface registration into a graph matching problem that combines both geometric and appearance similarities and intrinsic embedding informa- tion, 2) the first implementation of high-order graph matching algorithm that solves a non-convex optimization prob- lem, and 3) an efficient two-stage optimization approach to constrain the search space for dense surface registration. Our method is validated through a series of experiments demonstrating its accuracy and efficiency, notably in challenging cases of large and/or non-isometric deformations, or meshes that are partially occluded.

Links: Speaker info

120 Seminar 25-6-2010

Speaker: Arnau Ramisa ()
Title: Fast and Robust Object Segmentation with the Integral Linear Classifier
Abstract:

We propose an efficient method, built on the popular Bag of Features approach, that obtains robust multiclass pixellevel object segmentation of an image in less than 500ms, with results comparable or better than most state of the art methods. We introduce the Integral Linear Classifier (ILC),that can readily obtain the classification score for any image sub-window with only 6 additions and 1 product by fusing the accumulation and classification steps in a single operation. In order to design a method as efficient as possible, our building blocks are carefully selected from the quickest in the state of the art. More precisely, we evaluate the performance of three popular local descriptors, that can be very efficiently computed using integral images, and two fast quantization methods: the Hierarchical K-Means, and the Extremely Randomized Forest. Finally, we explore the utility of adding spatial bins to the Bag of Features histograms and that of cascade classifiers to improve the obtained segmentation. Our method is compared to the state of the art in the difficult Graz-02 and PASCAL 2007 Segmentation Challenge datasets.

Links: Speaker info

Seminar 17-6-2010

119 Speaker: Nicolás Rojas ()
Title: A robust forward kinematics analysis of 3-RPR planar platforms
Abstract:

The standard forward kinematics analysis of 3-RPR planar parallel platforms boils down to computing the roots of a sextic polynomial. There are many different ways to obtain this polynomial but all of them include exceptions for which the formulation is not valid. Unfortunately, near these exceptions the corresponding polynomial exhibits numerical instabilities. In this paper, we provide a way around this inconvenience by translating the forward kinematics problem to be solved into an equivalent problem fully stated in terms of distances. Using constructive geometric arguments, an alternative sextic -which is not linked to a particular reference frame- is straightforwardly obtained without the need of variable eliminations nor tangent-half-angle substitutions. The presented formulation is valid, without any modification, for any planar 3-RPR parallel platform, including the special architectures and configurations -which ultimately lead to numerical instabilities- that cannot be directly handled by previous formulations.

Links: Speaker info, ARK-2010

118 Speaker: Oriol Bohigas ()
Title: A Complete Method for Workspace Boundary Determination
Abstract:

This paper introduces a new method for workspace boundary determination on general lower-pair multi-body systems. The method uses a branch-and-prune technique to isolate the set of end effector singularities, and then classifies the points in such set according to whether they correspond to actual motion impediments in the workspace. The method can deal with open- or closed-chain systems, and is able to take joint limits into account. Advantages over other methods of similar applicability include its completeness and a simpler algorithmic structure. Examples are included that show its performance on benchmark problems documented in the literature.

Links: Speaker info, ARK-2010

117 Seminar 15-6-2010

Speaker: Pedro Lima ()
Title: Research on Networked Robot Systems at ISR/IST, Lisboa
Abstract:

In this talk I will describe the research activities at the Intelligent Systems Lab of the Institute for Systems and Robotics at Instituto Superior Técnico, Technical University of Lisbon. Most of the talk will concentrate on research done on networked robot systems, composed of static and mobile sensors that cooperate to carry out a diversified set of tasks. The group is particularly interested on discrete event models of robotic tasks, prone to qualitative (e.g., formal verification) and quantitative (e.g., plan probability of success) analysis, as well as on active cooperative perception and planning under uncertainty. A brief overview of past and running projects on soccer robots, rescue robots and mobile robots cooperating with a camera network to interact with people will be presented.

Links: Speaker info

116 Seminar 10-6-2010

Speaker: Júlia Borràs ()
Title: Singularity-invariant leg rearrangements in Stewart-Gough platforms
Abstract:

This work presents a necessary and sufficient condition to define a singularity-invariant leg rearrangement, based on an affine relation between the squared leg lengths before and after the rearrangement. This condition is then specified for four rigid components that can occur in Stewart-Gough platforms, leading to the characterization of singularity-invariant leg rearrangements on all of them.

Links: Speaker info, ARK-2010

115 Seminar 9-6-2010

Speaker: Stephan Strahl ()
Title: Experimental Study and Numerical Model Analysis of an Open Cathode Self Humidified PEMFC System
Abstract:

Open cathode, self humidified PEM fuel cell (OCSH-PEMFC) systems have an intrinsic* *advantage over conventional systems due to their minimal peripheral components and high system power densities, however they tend to be affected by environmental conditions, operating modes and load changes. The main reason for this sensitivity is water distribution. Thus, fundamental understanding of water distribution and transport is imperative in this highly dynamic system, which can be strongly affected by different control strategies. In this context, a two-dimensional, non-isothermal, dynamic model of an OCSH-PEMFC was developed and used in conjunction with a custom built test station and environmental chamber. With these tools a comprehensive analysis of the system performance under a wide range of environmental and operating conditions associated with their internal physical phenomena is presented. This model is used to simulate and study the effects of water transport/distribution and their influence on the system performance in order to develop new water management control strategies to improve efficiency and operating range of a commercially available OCSH-PEMFC system. The model consists of three sub-models based on energy, momentum and water mass balances of the system applied to anode, cathode channel, diffusion layers and MEA. The model is also applicable to other PEMFC systems, following the developed modeling strategy and performing the proposed experiments in order to determine the particular coefficients.

Links: Speaker info

Seminar 3-6-2010

114 Speaker: Michael Villamizar ()
Title: Efficient Rotation Invariant Object Detection using Boosted Random Ferns
Abstract:

We present a new approach for building an efficient and robust classifier for the two class problem, that localizes objects that may appear in the image under different orientations. In contrast to other works that adddress this problem using multiple classifiers, each one specialized for a specific orientation, or using multi-class classifiers, we propose a more straightforward approach that consists of two simple stages, namely estimation and classification. The estimator yields an initial object orientation hypothesis, which is then used by the classifier. False positive orientations provided by the estimator are rejected during the classification stage. This methodology allows reducing the time complexity of the algorithm while classification results remain high. The classifier we use in both stages is based on a boosted combination of Random Ferns over local histograms of oriented gradients (HOGs) in order to capture local statistics from gradient-based features. These are also another remarkable differences with respect to current methods where Random Ferns are computed over image intensity domain and without a supervised learning. We evaluate our method on standard databases, and show that it yields competitive results comparable to state-of-the-art approaches while being significantly more efficient. Furthermore, a new database for object detection under in-plane rotations is presented. This dataset contains motorbike instances with challenging conditions such as cluttered background, different illumination conditions and partial occlusions.

Links: Speaker info, CVPR-2010, Paper.

113 Speaker: Eduard Serradell ()
Title: Combining Geometric and Appearance Priors for Robust Homography Estimation
Abstract:

The homography between pairs of images are typically computed from the correspondence of keypoints, which are established by using image descriptors. When these descriptors are not reliable, either because of repetitive patterns or large amounts of clutter, additional priors need to be considered. The Blind PnP algorithm makes use of geometric priors to guide the search for matches while computing camera pose. Inspired by this, we propose a novel approach for homography estimation that combines geometric priors with appearance priors of ambiguous descriptors. More specifically, for each point we retain its best candidates according to appearance. We then prune the set of potential matches by iteratively shrinking the regions of the image that are consistent with the geometric prior. We can then successfully compute homographies between pairs of images containing highly repetitive patterns and even under oblique viewing conditions.

Links: Speaker info

112 Seminar 27-5-2010

Speaker: René Alquezar ()
Title: Comparing Error Minimized Extreme Learning Machines and Support Vector Sequential Feedforward Neural Networks for Classification Problems
Abstract:

Recently, error minimized extreme learning machines (EM-ELMs) have been proposed as a simple and efficient approach to build single-hidden-layer feedforward networks (SLFNs) sequentially. They add random hidden nodes one by one (or group by group) and update the output weights incrementally to minimize the sum-of-squares error in the training set. Other very similar methods that also construct SLFNs sequentially had been reported earlier with the main difference that their hidden-layer weights are a subset of the data instead of being random. By analogy with the concept of support vectors original of support vector machines (SVMs), these approaches can be referred to as support vector sequential feedforward neural networks (SV-SFNNs). An experimental study on ten benchmark classification data sets, comparing EM-ELMs and SV-SFNNs, was carried out under the same conditions for the two models. Although both models have the same (efficient) computational cost, a statistically significant improvement in generalization performance of SV-SFNNs vs. EM-ELMs was found in six out of the ten benchmark problems.

Links: Speaker info, Paper

111 Seminar 21-5-2010

Speaker: Raffaele Di Gregorio ()
Title: Determination of singularity loci properties in fully-parallel manipulators through Laplace expansions
Abstract:

In parallel mechanisms, singular configurations (singularities) have to be avoided during motion. All the singularities should be located in order to avoid them. Hence, relationships involving all the singular platform poses (singularity locus) and the mechanism geometric parameters are useful in the design of parallel mechanisms. A "simple" expression of the singularity condition of the most general mechanism (6-6 FPM) of a class of parallel mechanisms usually named fully-parallel mechanisms (FPM) is deduced by using a particular Laplace expansion of its Jacobian's determinant. The presented expression uses the mixed products of vectors that are easy to be identified on the mechanism. This approach will permit some singularities to be geometrically found. A procedure, based on this expression, is provided to transform the singularity condition into a ninth-degree polynomial equation whose unknowns are the platform pose parameters. This singularity polynomial equation is cubic in the platform position parameters and a sixth-degree one in the platform orientation parameters. Finally, how to derive the expression of the singularity condition of a specific FPM from the presented 6-6 FPM singularity condition will be shown along with an example.

Links: Speaker info, Slides

110 Seminar 12-5-2010

Speaker: Salvador de Lira ()
Title: PEM Fuel Cell Model oriented to Fault Diagnosis
Abstract:

PEM fuel cells are considered to have the highest energy density due to the nature of the reaction and the quickest start up time without pollution. These are the main reasons for being used in applications such as automotive engines, portable and backup power applications. Recent years have seen a proliferation of PEM FC system optimization and control applications, where the aim is to obtain a better process performance. This presentation addresses the problem of a model based fault diagnosis methodology for a PEM fuel cell system as case study. The methodology is based on computing residuals, which are obtained by PEM FC system model which has been calibrated using real data. The innovation of this methodology is based on the characterization of the relative residual fault sensitivity. To illustrate the results, a non-linear fuel cell simulator is used.

Links: Speaker info

Seminar 29-4-2010

109 Speaker: Júlia Borràs ()
Title: A Family of Quadratically-Solvable 5-SPU Parallel Robots
Abstract:

A 5-SPU robot with collinear universal joints is well suited to handling an axisymmetric tool, since it has 5 controllable DoFs and the remaining one is a free rotation around the tool. The kinematics of such a robot having also coplanar spherical joints has previously been studied as a rigid subassembly of a Stewart-Gough platform, it being denoted a line-plane component. It was shown that this component has 8 assembly modes corresponding to the roots of a bi-quartic polynomial. Here we identify a whole family of these 5-SPU robots having only 4 assembly modes, which are obtained by solving two quadratic equations. This family is defined by a simple proportionality constraint relating the coordinates of the base and platform attachments. A geometric interpretation of the architectural singularities of this type of robots in terms of conics is provided, which facilitates their avoidance at the design stage. Parallel singularities obey also a neat geometric structure, which permits deriving a cell decomposition of configuration space. Two practical features of these quadratically-solvable robots are the large maneuverability within each connected component and the fact that, for a fixed orientation of the tool, the singularity locus reduces to a plane.

Links: Speaker info, ICRA-2010

108 Speaker: Patrick Grosch ()
Title: Motion Planning for a Novel Reconfigurable Parallel Manipulator with Lockable Revolute Joints
Abstract:

This paper introduces a class of parallel robots consisting of a fixed base and a moving platform connected by serial chains having RR_PS (Revolute-Revolute-Prismatic-Spherical) topology. Only the prismatic joint is actuated and the first revolute joint in the chain can be locked or released online. The introduction of these lockable joints allow the prismatic actuators to manoeuver to approximate 6-DoF motions for the moving platform. An algorithm for generating these maneuvers is first described. Then, a motion planner, based on the generation of a Probabilistic RoadMap (PRM) whose nodes are connected using the presented maneuvers, is presented. The generated trajectories avoid singularities and possible collisions between legs.

Links: Speaker info, ICRA-2010

107 Seminar 22-4-2010

Speaker: Sergi Foix ()
Title: Object Modeling using a ToF Camera under an Uncertainty Reduction Approach
Abstract:

Time-of-Flight (ToF) cameras deliver 3D images at 25 fps, offering great potential for developing fast object modeling algorithms. Surprisingly, this potential has not been extensively exploited up to now. A reason for this is that, since the acquired depth images are noisy, most of the available registration algorithms are hardly applicable. A further difficulty is that the transformations between views are in general not accurately known, a circumstance that multi-view object modeling algorithms do not handle properly under noisy conditions. In this work, we take into account both uncertainty sources (in images and camera poses) to generate spatially consistent 3D object models fusing multiple views with a probabilistic approach. We propose a method to compute the covariance of the registration process, and apply an iterative state estimation method to build object models under noisy conditions.

Links: Speaker info, ICRA-2010

106 Seminar 21-4-2010

Speakers: Enric Asunción and Eduard Castañeda (, )
Title: PEM Fuel Cell Propulsion System
Abstract:

The project aim is based on the undeniable fact that nowadays humanity development is dramatically linked to the energy consumption. Our social and industrial way of life wastes in minutes what has need millions to be generated and creates residues that become hazardous thousands of years. But even thought, there is a bigger problem, there are not enought reserves that guarantee our growth level for the next half of the century. As a result we focus our end of degree project on Proton Exchange Membrane (PEM). PEM uses hydrogen as energetic vector to produce electricity by a simple catalytic process. To test that capabilities we will try to face to face our PEM prototype with a self transportation device with its own power supply system. This device target will be a Segway RMP-200 and more specifically the NiMH power supply batteries on the package.


The Project Step by Step:

  • Segway power supply characterization.

  • Modeling operating load profiles.

  • Pre-design of the power supply system using hydrogen fuel cells.

  • Implementation of the Power supply prototype.

  • Lab testing of the Prototype.

  • Final design.

  • On board Testing.

Links: Speaker info, Speaker info, Project Brochure

105 Seminar 8-4-2010 (Cancelled by the speaker)

Speaker: Oscar Sandoval ()
Title:
Abstract:
Links: Speaker info

104 Seminar 11-3-2010

Speaker: Alejandro Agostini ()
Title: Interactive Learning of Action Rules for Decision Making
Abstract:

Learning action rules on-line, from an empty rule base, and using them immediately for decision making involve many difficulties. On-line learning demands techniques that rapidly adjust their estimation with new incoming examples. Additionally, if decision making is to occur intertwined with learning, learned rules should become available as soon as possible to assure an improvement in performance, and to achieve the goal in a reasonable amount of time. For this, rules should be learned from few experiences of actions relevant for the task, and decision making should not be time consuming.

Most machine learning techniques are designed to work in batch mode, i.e. when the samples for learning are available in advance, and use an iterative process that requires a significant amount of data and computation. The mentioned requirements for rule learning and decision making pose obstacles for the existing rule learning paradigms, where on-line learning is not considered, a significant amount of prior knowledge has to be provided, or a large number of experiences are required.

This talk proposes a method for on-line learning of action rules from few observations using a rule performance evaluation based on densities. It is also presented a method for macro rules generation using a new technique of condition propagation that avoids large computational cost to make decisions. Macro rules could significantly reduce the amount of deliberation as they might merge repetitive sequences of actions, or plans found with a large computational cost. Other approaches have focused on the generation of macros such as primitive behaviors, macro-actions, or activation rules. They explore different long-term acting behaviors to select the one suitable for the task which requires a large amount of computation.

The learning mechanisms are embedded in a Decision Making Framework (DMF) that closes effectively the planning-learning loop. The DMF integrates: the rule learning methods, a planner, and an expert teacher that guides action executions.

Links: Speaker info

103 Seminar 3-3-2010

Speaker: Ana Gabriela Guzmán ()
Title: Técnicas y Herramientas para el análisis de sistemas de gran escala: Aplicación a la Red de Transporte de Aguas de Barcelona
Abstract:

See the associated document.

Links: Abstract

102 Seminar 2-2-2010

Speaker: Vanesa García ()
Title: Modelling and control of catalytic reactors for the production of of hydrogen for PEM fuel cells
Abstract:

See the associated document.

Links: Abstract

101 Seminar 21-1-2010

Speaker: Sergi Foix ()
Title: ToF cameras applied to 3D object modelling
Abstract:

This Master's thesis has two main contributions. Firstly, reviews the state-of-the art in the field of Time-of-Flight (ToF) cameras, their advantages, their limitations, the existing calibration methods and their present-day applications sometimes in combination with other sensors. And secondly, proposes a method for object modelling using a ToF camera under an uncertainty reduction approach.
Even though ToF cameras provide neither higher resolution nor larger ambiguity-free range compared to other range map estimation systems, advantages such as registered depth and intensity data at a high frame rate, compact design, low weight and reduced power consumption have motivated their use in numerous areas of research. In robotics, these areas range from mobile robot navigation and map building to vision-based human motion capture and gesture recognition, showing particularly a great potential in object modelling and recognition. ToF cameras deliver 3D images at 25 fps, offering great potential for developing fast object modelling algorithms. Surprisingly, this potential has not been extensively exploited up to now. A reason for this is that, since the acquired depth images are noisy, most of the available registration algorithms are hardly applicable. A further difficulty is that the transformations between views are in general not accurately known, a circumstance that multiview object modelling algorithms do not handle properly, especially under noisy conditions. In this work, both uncertainty sources (in images and camera poses) to generate spatially consistent 3D object models fusing multiple views with a probabilistic approach are taken into account. A method to compute the covariance of the registration process is proposed, and a statistical framework applied to build object models under noisy conditions.

Links: Master Project

100 Seminar 17-12-2009

Speaker: Aleix Martínez ()
Title: Bayes Optimal Pattern Recognition
Abstract:

Many engineering and scientific problems can be formulated as a pattern recognition one. In such problems, linear methods are preferred for their simplicity and tractability. Unfortunately, linear methods have many limitations, of which several are still unknown. Understanding these limitations is key to advancing the current state of the art. In this talk, we will address these issues within the context of feature extraction and classification. We will define when linear feature extraction methods do not work and how this knowledge can be used to propose algorithms that are guaranteed to work in a large number of applications. Several results in vision, linguistics, and the modeling of the primary visual cortex will be presented. Time permitting, we will sketch the problem posed by classical normalization procedures. In particular, that of norm normalization, generally used to make texture invariant to the intensity of the light source, shape to scale and rotation, and for modeling mtDNA in genetics. This result will lead us to the definition on of new type of kernels -- Rotation Invariant Kernels -- which are specially useful in shape analysis problems. Open problems will be outlined during the course of the talk.

Links: Web page

99 Seminar 3-12-2009

Speaker: Rafael Valencia ()
Title: 3D Mapping for Urban Service Robots
Abstract:

We present an approach to the problem of 3D map building in urban settings for service robots, using three dimensional laser range scans as the main sensor data input. Our system is based on the probabilistic alignment of 3D point clouds employing a delayed-state information-form SLAM algorithm, for which we can add observations of relative robot displacement efficiently. These observations come from the alignment of dense range data point clouds computed with a variant of the iterative closest point algorithm. The datasets were acquired with our custom built 3D range scanner integrated into a mobile robot platform. Our mapping results are compared to a GIS-based CAD model of the experimental site. The results show that our approach to 3D mapping performs with sufficient accuracy to derive traversability maps that allow our service robots navigate and accomplish their assigned tasks on a urban pedestrian area.

Links: IROS-09

98 Seminar 2-12-2009

Speaker: Vicenç Puig ()
Title: Resultados y Perspectivas en el modelado y control de redes relacionadas con el ciclo integral del agua
Abstract:

En esta charla, Vicenç hablará acerca de diversos trabajo desarrollados (o en desarrollo) dentro de las actividades del grupo SAC (Sistemes Avançats de Control), todos ellos relacionados con el modelado y control de redes de agua potable, redes de alcantarillado y redes de canales y riego. Adicionalmente, se comentarán diversos matices en cuanto a investigaciones no sólo ligadas con el modelado dinámico y gestión de tales redes, sino también temas de calidad del agua, propagación de cloro, eficiencia de gestión de redes, entre otros tópicos.

Links:

97 Seminar 19-11-2009

Speaker: Peter Stone ()
Time: 16:00
Title: Intersections of the Future: Using Fully Autonomous Vehicle
Abstract:

Artificial intelligence research is ushering in a new era of sophisticated, mass-market transportation technology. While computers can already fly a passenger jet better than a trained human pilot, people are still faced with the dangerous yet tedious task of driving automobiles. Intelligent Transportation Systems (ITS) is the field of study that aims to use artificial intelligence to make transportation safer, cheaper, and more efficient. Recent advances in ITS point to a future in which vehicles themselves handle the vast majority of the driving task. Once autonomous vehicles become popular, autonomous interactions amongst *multiple* vehicles will be possible. Current methods of vehicle coordination, which are all designed to work with human drivers, will be outdated. The bottleneck for roadway efficiency will no longer be the drivers, but rather the mechanism by which those drivers' actions are coordinated. While open-road driving is a well-studied and more-or-less-solved problem, urban traffic scenarios, especially intersections, are much more challenging.
This talk will address the question: To what extent and how can a multiagent intersection control mechanism take advantage of the capabilities of autonomous vehicles in order to make automobile travel safer and faster? First, I will introduce and specify the problem of intersection management as a multiagent system and define a metric by which solutions can be evaluated. Next, I will propose a novel multiagent intersection control mechanism in which autonomous driver agents call ahead and reserve space-time in the intersection, pending the approval of an arbiter agent called an intersection manager, which is located at the intersection.
Experiments indicate that our method can reduce delays at intersections by up to two orders of magnitude.

Links: Web page

96 Seminar 5-11-2009

Speaker: Jacques Penders
Title: A robot Swarm assisting a Human Fire Fighter
Abstract:

Emergencies in industrial warehouses are a major concern for fire fighters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges.
The Guardians robot swarm is designed to assist fire fighters in searching a large warehouse. In this talk I discuss the technology developed for a swarm of robots assisting fire fighters. I will briefly explain the swarming algorithms which provide the functionality by which the robots react to and follow humans. Next I discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also the means to locate the robots and humans. Thus the robot swarm is able to provide guidance information to the humans. Together with the fire fighters the team explored how the robot swarm should feed information back to the human fire fighter. We have designed and experimented with interfaces for presenting swarm based information to human beings.
To conclude: the interaction from the human to the robots is very distinct from the interaction of the robots with the human.

  1. Simply by moving around a human being provokes reactions from the autonomous robots; the swarming algorithms provide this functionality. Thus the human to robots swarm interface requiring very little cognitive effort.

  2. The robots can transfer guidance information to the human. In collaboration with the fire fighters several interfaces for obtaining guidance from the surrounding robot swarm have been designed and tested. Nevertheless, it is an outstanding issue whether a feel of confidence can be created.

Links: Project

95 Seminar 21-10-2009

Speaker: Sergey V. Ablameyko ()
Title: Joint interpretation of remote sensing images and digital maps
Abstract:

Not disclosed.

Links: Web page

Seminar 9-10-2009

94 Speaker: Andreu Corominas ()
Title: Integrating Asynchronous Observations for Mobile Robot Position Tracking in Cooperative Environments
Abstract:

This paper presents an asynchronous particle filter algorithm for mobile robot position tracking, taking into account time considerations when integrating observations being delayed or advanced from the prior estiamate time point. The interest of that filter lies in cooperative environments and in fast vehicles. The paper studies the first case, where a sensor network shares perception data with running robots that receive accurate obeservations with large delays due to acquisition, processing and wireless communications. Promising simulated results comparing a basic particle filter and the proposed one are shown. The paper also investigates a situation where a robot is tracking its position, fusing only odometry and observations from a camera network partially covering the robot path.

Links: IROS-09


93 Speaker: Juan Andrade-Cetto ()
Title: Calibrating an Outdoor Distributed Camera Network Using Laser Range Finder Data
Abstract:

Outdoor camera networks are becoming ubiquitous in critical urban areas of large cities around the world. Although current applications of camera networks are mostly limited to video surveillance, recent research projects are exploiting advances on outdoor robotics technology to develop systems that put together networks of cameras and mobile robots in people assisting tasks. Such systems require the creation of robot navigation systems in urban areas with a precise calibration of the distributed camera network. Despite camera calibration has been an extensively studied topic, the calibration (intrinsic and extrinsic) of large outdoor camera networks with no overlapping view fields, and likely to suffer frequent recalibration, poses novel challenges in the development of practical methods for user-assisted calibration that minimize intervention times and maximize precision. In this paper we propose the utilization of Laser Range Finder (LRF) data covering the area of the camera network to support the calibration process and develop a semi-automated methodology allowing quick and precise calibration of large camera networks. The proposed methods have been tested in a real urban environment and have been applied to create direct mappings (homographies) between image coordinates and world points in the ground plane (walking areas) to support person and robot detection and localization algorithms.

Links: IROS-09

92 Seminar 7-10-2009

Speaker: Attila Husar ()
Title: Dynamic Water Management Test Station for Open-Cathode PEMFC Systems
Abstract:

Hydrogen fed polymer electrolyte membrane fuel cells (PEMFC) are energy conversion devices that can be implemented into a wide range of applications such as automotive, stationary and portable systems. However, to optimize performance, they require active control and thus in-depth understanding of the system dynamics. Understanding water transport mechanisms through the membrane and membrane water content is one of the keys to improving PEMFC system performance. Once these mechanisms are identified and modelled, effective control strategies can be implemented. Hence, the main objective of this test station design is to experimentally characterize the water transport mechanisms through the membrane and its water content for the purpose of studying their dynamic influence on fuel cell system performance for numerical model verification and validation. The analysis will then lead to an in-depth understanding of how a system with minimal parts and complexity can be improved in terms of overall efficiency, stability and operating rage. These conclusions will provide guidelines to develop a proper control strategy with the necessary sensors to ensure that the system operates properly under a wide range of environmental conditions.

Links:

Seminar 1-10-2009

91 Speaker: Anaís Garrell ().
Title: Discrete Time Motion Model for Guiding People in Urban Areas Using Multiple Robots
Abstract:

We present a new model for people guidance in urban settings using one or several mobile robots, that overcomes the limitations of existing approaches, which are either tailored to tightly bounded environments, or based on unrealistic human behaviors. Although the robots motion is controlled by means of a standard particle filter formulation, the novelty of our approach resides in how the environment and human and robot motions are modelled. In particular we define a Discrete-Time-Motion model, which from one side represents the environment by means of a potential field, that makes it appropriate to deal with open areas, and on the other hand the motion models for people and robots respond to realistic situations, and for instance human behaviors such as leaving the group are considered.

Links: IROS-09


90 Speaker: Guillem Alenyà ()
Title: A Comparison of Three Methods for Measure of Time to Contact
Abstract:

Time to Contact (TTC) is a biologically inspired method for obstacle detection and reactive control of motion that does not require scene reconstruction or 3D depth estimation. Estimating TTC is difficult because it requires a stable and reliable estimate of the rate of change of distance between image features. In this paper we propose a new method to measure time to contact, Active Contour Affine Scale (ACAS). We experimentally and analytically compare ACAS with two other recently proposed methods: Scale Invariant Ridge Segments (SIRS), and Image Brightness Derivatives (IBD). Our results show that ACAS provides a more accurate estimation of TTC when the image flow may be approximated by an affine transformation, while SIRS provides an estimate that is generally valid, but may not always be as accurate as ACAS, and IBD systematically over-estimate time to contact.

Links: IROS-09

Seminar 17-9-2009

89 Speaker: Guillem Alenyà ()
Title: Time To Contact for Obstacle Avoidance
Abstract:

Time to Contact (TTC) is a biologically inspired method for obstacle detection and reactive control of motion that does not require scene reconstruction or 3D depth estimation. TTC is a measure of distance expressed in time units. Our results show that TTC can be used to provide reactive obstacle avoidance for local navigation. In this paper we describe the principles of time to contact and show how time to contact can be measured from the rate of change of size of features. We show an algorithm for steering a vehicle using TTC to avoid obstacles while approaching a goal. We present the results of experiments for obstacle avoidance using TTC in static and dynamic environments.

Links: ECMR-09


88 Speaker: Juan Andrade-Cetto ()
Title: Amortized Constant Time State Estimation in SLAM using a Mixed Kalman-Information Filter
Abstract:

The computational bottleneck in all information-based algorithms for SLAM is the recovery of the state mean and covariance. The mean is needed to evaluate model Jacobians and the covariance is needed to generate data association hypotheses. Recovering the state mean and covariance requires the inversion of a matrix of the size of the state. Current state recovery methods use sparse linear algebra tools that have quadratic cost, either in memory or in time. In this paper, we present an approach to state estimation that is worst case linear both in execution time and in memory footprint at loop closure, and constant otherwise. The approach relies on a state representation that combines the Kalman and the information-based state representations. The strategy is valid for any SLAM system that maintains constraints between robot poses at different time slices. This includes both Pose SLAM, the variant of SLAM where only the robot trajectory is estimated, and hierarchical techniques in which submaps are registered with a network of relative geometric constraints.

Links: ECMR-09

87 Seminar 10-9-2009

Speaker: Agustin Ortega ()
Title: Graph-based Segmentation of Range Data with Applications to 3D Urban Mapping
Abstract:

This paper presents an efficient graph-based algorithm for the segmentation of planar regions out of 3D range maps of urban areas. Segmentation of planar surfaces in urban scenarios is challenging because the data acquired is typically sparsely sampled, incomplete, and noisy. The algorithm is motivated by Felzenszwalb's algorithm to 2D image segmentation , and is extended to deal with non-uniformly sampled 3D range data using an approximate nearest neighbor search. Inter-point distances are sorted in increasing order and this list of distances is traversed growing planar regions that satisfy both local and global variation of distance and curvature. The algorithm runs in O(n log n) and compares favorably with other region growing mechanisms based on Expectation Maximization. Experiments carried out with real data acquired in an outdoor urban environment demonstrate that our approach is well-suited to segment planar surfaces from noisy 3D range data. A pair of applications of the segmented results are shown, a) to derive traversability maps, and b) to calibrate a camera network.

Links: ECMR-09

86 Seminar 9-7-2009

Speaker: Ernesto Homar Teniente ()
Title: Autonomous 3D Map Building for Urban Settings with Range Data
Abstract:

This thesis proposal addresses the problem of building maps for mobile robots that navigate in outdoor urban areas. And we wish to do so as autonomously as possible. In this context, our robot must be able to build a locally detailed and globally consistent map using 3D range sensing as the primary source of information. The robot must be able to determine its position with 6 degrees-of-freedom (DOF) within this map. This task is known in the robotics literature as Autonomous Simultaneous Localization and Mapping. Furthermore, from the acquired maps, the robot must be able to perform traversability analyses to take informed decissions for navigation and exploration. Once the map is built some post-processing might be necessary to obtain a complete digital compact volumetric model, merging information to reduce the memory space of the map without compromising details and consistency. The presentation includes a brief analysis of the state of the art in the topic, as well as some initial contributions on efficient algorithms for range data registration, map building in the context of the URUS project, and applications of the produced maps to traversability analysis and camera network calibration.

Links:

85 Seminar 2-7-2009

Speaker: Carlos Rosales ()
Title: Motion planning for high DOF anthropomorphic hands
Abstract:

The talk deals with the problem of motion planning of anthropomorphic mechanical hands avoiding collisions. The approach that will be presented tries to mimic the real human hand motions, but reducing the dimension of the search space in order to obtain results as a compromise between motion optimality and planning complexity (time) by means of the concept of principal motion directions. Basically, the work includes the following phases: capturing the human hand workspace using a sensorized glove and mapping it to the mechanical hand workspace, reducing the space dimension by looking for the most relevant principal motion directions, and planning the hand movements using a sampling-based roadmap planner. The approach has been implemented for a four finger anthropomorphic mechanical hand, and some examples are will be shown to illustrate its validity.

Links:

84 Seminar 4-6-2009

Speaker: Frank Dellaert ().
Title: Inference in Large-Scale Graphical Models and its application to SFM, SAM, and SLAM
Abstract:

Simultaneous Localization and Mapping (SLAM), Smoothing and Mapping (SAM), and Structure from Motion (SFM) are important and closely related problems in robotics and vision. Not surprisingly, there is a large literature describing solutions to each problem, and more and more connections are established between the two fields. At the same time, robotics and vision researchers alike are becoming increasingly familiar with the power of graphical models as a language in which to represent inference problems. In this talk I will show how SFM, SAM, and SLAM can be posed in terms of this graphical model language, and how inference in them can be explained in a purely graphical manner via the concept of variable elimination. I will then present a new way of looking at inference that is equivalent to the junction tree algorithm yet is - in my view- much more insightful. I will also show that, when applied to linear(ized) Gaussian problems, the algorithm yields the familiar QR and Cholesky factorization algorithms, and that this connection with linear algebra leads to strategies for very fast inference in arbitrary graphs. I will conclude by showing some published and preliminary work that exploits this connection to the fullest.

Links:

83 Seminar 21-5-2009

Speaker: Michael Villamizar ()
Title: Combining Color-Based Invariant Gradient Detector with HoG Descriptors
for Robust Image Detection in Scenes under Cast Shadows
Abstract:

In this work we present a robust detection method in outdoor scenes under cast shadows using color based invariant gradients in combination with HoG local features. The method achieves good detection rates in urban scene classification and person detection outperforming traditional methods based on intensity gradient detectors which are sensible to illumination variations but not to cast shadows. The method uses color based invariant gradients that emphasize material changes and extract relevant and invariant features for detection while neglecting shadow contours. This method allows to train and detect objects and scenes independently of scene illumination, cast and self shadows. Moreover, it allows to do training in one shot, that is, when the robot visits the scene for the first time.

Links:

82 Seminar 23-4-2009

Speaker: Viorela Ila ()
Title: Information-based Compact Pose SLAM
Abstract:

Pose SLAM is the variant of SLAM where only the robot trajectory is estimated and where landmarks are only used to produce relative constraints between robot poses. To reduce the computational cost of the information filter form of Pose SLAM and, at the same time, to delay inconsistency as much as possible, we introduce an approach that only takes into account highly informative loop closure links and non redundant poses. The technique includes constant time procedures to compute the distance between poses and the potential information gain obtained from each link, as well as an approximately linear procedure in terms of time and memory, to recover the state after a loop closure. Using these procedures, the robot operates most of the time in open loop, amortizing the cost of loop closure over long trajectories. In this case, the computational bottleneck shifts to data association, that is, to a search over the set of previously visited poses to determine good candidates for sensor registration. To speed up data association, we introduce a method to search for neighboring poses whose complexity ranges from logarithmic in the usual case to linear in degenerate situations. The method is based on organizing the pose information in a balanced tree whose internal levels are defined using interval arithmetic. The proposed method is validated through simulations, experiments using standard SLAM data sets, and real mapping sessions.

Links:

81 Seminar 16-4-2009

Speaker: Júlia Borràs ()
Title: Kinematics of Line-Plane Subassemblies in Stewart Platforms
Abstract:

When the attachments of five legs in a Stewart platform are collinear on one side and coplanar on the other, the platform is said to contain a line-plane subassembly. This talk is devoted to the kinematics analysis of this subassembly paying particular attention to the problem of moving the aforementioned attachments without altering the singularity locus of the platform. It is shown how this is always possible provided that some cross-ratios between lines ---defined by points in the plane--- are kept equal to other cross-ratios between points in the line. This result leads to two simple motion rules upon which complex changes in the location of the attachments can be performed. These rules have interesting practical consequences as they permit a designer to optimize aspects of a parallel robot containing the analyzed subassembly, such as its manipulability in a given region, without altering its singularity locus.

Links:

80 Seminar 2-4-2009

Speaker: Stan Matwin ()
Title: Computer Ethics and Professional Responsibility Course for Computer Professionals
Abstract:

In this presentation I will discuss a course I have developed at the University of Ottawa, Canada, that covers the ethical, legal and social issues important for computer professionals. I will describe the contents of the course that include: an introduction to computer ethics and how to apply it in practice, privacy in the electronic era, social implications of the Internet, computer failures and legal responsibility, professional codes of conduct, global issues including digital divide, and the ethical issues related to robotics and autonomous computing. I will describe my experience and the student experience from three years of teaching this course in Ottawa. I will discuss a sample textbook and exam material for this course. I will argue that such a course (already a compulsory component of the ACM/IEEE curriculum in all computer science and engineering disciplines) truly belongs, for academic and moral reasons, to any modern information technology program.

Links: Web page, Slides.

79 Seminar 19-3-2009 (Cancelled by the speaker)

Speaker: Joan Solà ()
Title:
Abstract:
Links:

78 Seminar 12-3-2009

Speaker: Paul Verschure ()
Title: Brain based models for robot perception, cognition and behavior
Abstract:

The brain is still the most advanced system for perception, cognition and control that we know. Unfortunately we still do not have a theory of the brain that would allow us to copy neuronal principles to artifacts. Over the last few years, however, a number of inroads have been made in the areas of perception, learning and motor control. In my talk I will focus on our own work within the framework of the Distributed Adaptive Control architecture and its implications for our understanding of the neuronal principles underlying real-world perceptual systems, mapping and localization and the planning of discrete motor actions and the generalization of these principles towards robots, avatars and neuroprosthetic systems.

Links: Lab web page

77 Seminar 5-3-2009

Speaker: Alba Pérez ()
Title: Some open problems in kinematic synthesis
Abstract:

Kinematic synthesis aims to solve the function-to-form problem when dealing with motion. It has its origins in the graphical design of planar mechanisms and it is now applied to spatial mechanisms and robotic systems. One of the branches of kinematic synthesis is dimensional synthesis, in which the structure (also called topology) of the system is given and the goal is to dimension it in order to accomplish a given kinematic task.
In this talk, I want to present some of the unsolved problems within the dimensional synthesis of kinematic chains for spatial motion. A brief introduction to different approaches and their limitations will be given, to then focus on the use of Clifford algebras to express the synthesis problem. Several applications in the field of robot design and motion identification will be presented.

Links:

76 Seminar 26-2-2009

Speaker: Lina M. Paz ()
Title: Fast 3D Maps using Vision Only
Abstract:

In this talk I will describe a system that can carry out SLAM in large indoor and outdoor environments using a stereo pair moving with 6DOF as the only sensor. Unlike current visual SLAM systems that use either bearing-only monocular information or 3D stereo information, our system accommodates both monocular and stereo. Textured point features are extracted from the images and stored as 3D points if seen in both images with sufficient disparity, or stored as inverse depth points otherwise. This allows to map both near and far features: the first provide distance and orientation, and the second orientation information. Unlike other vision only SLAM systems, stereo does not suffer from scale drift because of unobservability problems, and thus no other information such as gyroscopes or accelerometers is required in our system. Our SLAM algorithm generates sequences of conditionally independent local maps that can share information related to the camera motion and common features being tracked. The system computes the full map using the novel Conditionally Independent Divide and Conquer algorithm, which allows constant time operation most of the time, with linear time updates to compute the full map. To demonstrate the robustness and scalability of our system, I will show experimental results in indoor and outdoor urban environments of 210m and 140m loop trajectories, with the stereo camera being carried in hand by a person walking at normal walking speeds of 4-5km/hour.

Links: Abstract in PDF, Web.

75 Seminar 6-2-2009

Speaker: René Motro ()
Title: Structural Morphology of Tensegrity Systems and Foldability
Abstract:

The coupling between form and forces, their structural morphology, is a key point for tensegrity systems. In the first part of this paper we describe the design process of the simplest tensegrity system which was achieved by Kenneth Snelson. Some other simple cells are presented and tensypolyhedra are defined as tensegrity systems which meet polyhedra geometry in a stable equilibrium state. A numerical model giving access to more complex systems, in terms of number of components and geometrical properties, is then evoked. The third part is devoted to linear assemblies of annular cells -tensegrity rings- which can be folded. Some experimental models of the tensegrity ring which is the basic component of this "hollow rope" have been realized and are examined. The last part is devoted to some possible foldable tensegrity systems.

Links:

74 Seminar 11-12-2008

Speaker: Juan Cortés ()
Title: Path Planning in Structural Biology
Abstract:

Path planning is an active field of research in robotics since the 70s. The addressed problem consists of computing feasible motions for a system (the robot) in a workspace cluttered with obstacles. In recent years, path planning techniques have undergone considerable progress. Sampling-based algorithms are efficient and general tools for exploring constrained high-dimensional spaces. Such algorithms have been successfully applied to challenging problems in diverse domains, including computational structural biology. This seminar gives an overview of recent works carried out at LAAS-CNRS in this domain. In particular, I will present path-planning-based algorithms for computing protein loop/domain motions and protein-ligand access/exit pathways. I will also show encouraging results obtained with these methods on real application examples.

Links:

73 Seminar 4-12-2008

Speaker: Francesc Moreno ()
Title: EPnP: An Accurate O(n) Solution to the PnP Problem
Abstract:

I will present a non-iterative solution to the PnP problem --the estimation of the pose of a calibrated camera from n 3D-to-2D point correspondences-- whose computational complexity grows linearly with n. This is in contrast to state-of-the-art methods that are O(n^5) or even O(n^8), without being more accurate. Our method is applicable for all n>=4 and handles properly both planar and non-planar configurations. Our central idea is to express the n 3D points as a weighted sum of four virtual control points. The problem then reduces to estimating the coordinates of these control points in the camera referential, which can be done in O(n) time by expressing these coordinates as weighted sum of the eigenvectors of a 12x12 matrix and solving a small constant number of quadratic equations to pick the right weights. Furthermore, if maximal precision is required, the output of the closed-form solution can be used to initialize a Gauss-Newton scheme, which improves accuracy with negligible amount of additional time. The advantages of our method are demonstrated by thorough testing on both synthetic and real-data.

Links:

72 Seminar 27-11-2008

Speaker: Miquel Ferrer ()
Title: Graph Embedding in Vector Spaces. Application to the Median Graph Computation.
Abstract:

Graphs have very interesting properties for object representation in pattern recognition. However, graph matching algorithms are usually computationally complex. In addition, graphs are harder to manipulate and operate than feature vectors. In the last years, some attempts have been made to combine the best of the graph and the vector domains in order to get the advantages of both worlds. In this sense, kernels offer an elegant way to embed graphs into vector spaces. In this work, we show how graph embedding can be used to obtain accurate and efficient approximations of the median graph. The median graph can be seen as the representative of a set of graphs but its application has been very limited up to now due to computational reasons. With this new approach, we can obtain an approximate median graph using real databases containing large graphs.

Links:

71 Seminar 6-11-2008

Speaker: Léonard Jaillet ()
Title: Transition-based RRT for Path Planning in Continuous Cost Spaces
Abstract:

We present a method called Transition-based RRT (T-RRT) for path planning in continuous cost spaces. It combines the exploration strength of the RRT algorithm that rapidly grow random trees toward unexplored regions of the space, with the efficiency of stochastic optimization methods that use transition tests to accept or to reject a new potential state. This planner also relies on the notion of minimal work path that gives a quantitative way to compare path costs. The method integrates self tuning ofa parameter controlling its exploratory behavior. It yields to solution paths that efficiently follow low cost valleys and saddle points of the cost space. Simulation results show that the method can be applied to a large set of applications including terrain costmap motions or planning low cost motions for freeflying or articulated robots.

Links:

70 Seminar 28-10-2008

Speaker: Vincent Lepetit ()
Title: Computer Vision and Augmented Reality
Abstract:

I will present recent advances in Computer Vision for Augmented Reality, and their commercial applications. I will also discuss the limits of the current techniques, and the possible directions to go beyond.

Links: Web page

69 Seminar 24-10-2008

Speaker: Alejandro Agostini ()
Title: Online Reinforcement Learning using Samples Density Estimations
Abstract:

Online reinforcement learning with generalization is a problem still unsolved for many complex applications. The main obstacles are produced by the high non-stationarity of the estimated cumulative reward, the online nature of the learning with non-uniform sampling, and the large amount of generalization required for a feasible implementation. Our previous approaches to face the RL problem consisted in estimating the probability distribution of the cumulative reward in coarse discrete partitions of the state space using a rule-based representation. The discrete partition and the biased sampling inherent to RL produced bad estimations of the cumulative reward due to the limited information about the real distribution of data that fed the rules. To overcome this inconvenient we propose a new RL approach based on a joint distribution estimation of the samples (state, action, cumulative reward) experienced. From this joint distribution it is possible to estimate the distribution of the cumulative reward with the highest confidence possible given the data experienced so far. The joint distribution is modelled with a Gaussian Mixture estimated with the online expectation-maximization algorithm and with active unit manipulation.

Links:

68 Seminar 2-7-2008

Speaker: Christophe Jermann ()
Title: From numerical analysis to interval constraint solving - a tutorial with case studies in robotics
Abstract:

Interval constraint solving techniques aim at returning an as-precise-as-desired framing of all the solutions to a system of equations and inequalities over the real numbers. They derive from numerical analysis and artificial intelligence: the numerical computations are performed on intervals instead of real numbers to avoid the inaccuracy of floating-point numbers computations and to enable a complete exploration of the search space; smart filtering techniques are employed in order to avoid searching the solutions in inappropriate regions of the search space. This talk will introduce these techniques and present their application to some robotics problems studied in the MEO group of the LINA lab to which the speaker belongs.

Links: Home page

67 Seminar 19-6-2008

Speaker: Fernando de la Torre ()
Title: Learning the representation for modeling, classification and clustering problems with energy-based component analysis methods
Abstract:

Selecting a good representation of the data is a key aspect of the success of any modeling, classification or clustering algorithm. Component Analysis (CA) methods (e.g. Kernel Principal Component Analysis, Independent Component Analysis, Tensor factorization) have been used as a feature extraction step for modeling, classification and clustering in numerous visual, graphics and signal processing tasks over the last four decades. CA techniques are especially appealing because many can be formulated as eigen-problems, offering great potential for efficient learning of linear and non-linear representations of the data without local minima. However, the eigen-formulation often hides important aspects of making the learning successful such as understanding normalization factors, how to build invariant representations (e.g. to geometric transformation), effects of noise and missing data or how to learn the kernel. In this talk, I will describe a unified framework for energy-based learning in CA methods. I will point out how apparently different learning tasks (clustering, classification, modeling) collapse into a single task when viewed from the perspective of energy functions. Moreover, I will propose several extensions of CA methods to learn linear and non-linear representations of data to improve performance, over the current use of CA features, in state-of-the-art algorithms for classification (e.g. support vector machines), clustering (e.g. spectral graph methods) and modeling/visual tracking (e.g. active appearance models) problems.
During the talk I will also review applications of CA techniques to depression assestment, deception detection and hot-flash prediction from multimodal sensors.

Links: Home page

66 Seminar 4-6-2008

Speaker: Cornel Sultan ()
Title: Tensegrity in Deployable Structures and Motion Simulations Applications
Abstract:

A brief summary of the history of tensegrity structures opens this talk, which summarizes the spaker's personal perspective on tensegrity structures. The technical part of the presentation focuses on two topics which have been investigated by the speaker in the past: tensegrity structures deployment and their use as motion base for flight simulators as replacements for the classical Stewart platform. The talk is concluded with considerations on tensegrity structures future and the challenges they face, especially in the field of controllable structures applications.

Links: Home page

65 Seminar 29-5-2008

Speaker: Alberto Sanfeliu ()
Title: First year report of the URUS project
Abstract:

This talk gives a brief account of the activities developed during the first year of the URUS project
The talk was previously given in the ICRA'08 workshop Network Robot Systems: benchmarks and platforms toward Human-Robot Interaction, co-organized by Alberto Sanfeliu.

Links: URUS project, ICRA08 workshop

64 Seminar 8-5-2008

Speaker: Júlia Borràs ()
Title: Architecture Singularities in Flagged Parallel Manipulators
Abstract:

Flagged manipulators are of interest because they are the only Stewart-Gough platforms for which a cell decomposition of their singularity loci is available. Here we show that the known family of such manipulators can be enlarged if one allows robot designs that, for some particular parameter values, become architecturally singular. Along this line, the most general 6-6 flagged manipulator is derived by applying a singularity-preserving transformation that leaves the relative position between two lines invariant. This transformation opens up the possibility of an "equal cross ratios" architectural singularity, which is shown to appear clearly in the factorization of the jacobian determinant. From the 6-6 flagged manipulator, all the extended family of (possibly architecturally-singular) flagged manipulators is derived.

Links: ICRA08 paper

63 Seminar 24-4-2008

Speaker: Andreu Corominas ()
Title: Efficient Active Global Localization for Mobile Robots Operating in Large and Cooperative Environments
Abstract:

This paper presents a novel and efficient framework to the active map-based global localization problem for mobile robots operating in large and cooperative environments. The paper proposes a rational criteria to select the action that minimizes the expected number of remaining position hypotheses, for the single robot case and for the cooperative case, where the lost robot takes advantage of observations coming from a sensor network deployed on the environment or from other localized robots. Efficiency in time complexity is achieved thanks to reasoning in terms of the number of hypotheses instead of in terms of the belief function. Simulation results in a real outdoor environment of 10.000m2 are presented validating the presented approach and showing different behaviors for the single robot case and for the cooperative one.

Links: ICRA08 paper

62 Seminar 10-4-2008

Speaker: Pablo Jimenez ()
Title: Survey on Model-based Manipulation Planning of Deformable Objects
Abstract:

Manipulation of deformable objects constitutes a challenge and a huge field of opportunities for roboticists, due to the richness in behaviour and the extended presence of such objects in everyday life. Long-term and goal-directed interaction of a robot with such objects implies to provide some kind of model-based planning capabilities. Whereas manipulation planning of rigid objects, viewed as an offspring of Motion Planning, is a well-studied subject, works dealing about manipulation planning of deformable objects are spread over a wide range of apparently unconnected application areas. This survey constitutes a first attempt of bringing results together and to present a systematic approach to the subject. The interested researcher will find here how such objects are currently modelled, which basic planning tools are available, how application-dependent constraints are dealt with and considered in planning, as well as a lot of inspiring examples.

Links:

61 Seminar 27-3-2008

Speaker: Carlos Rosales ()
Title: Finding All Valid Hand Configurations for a Given Precision Grasp
Abstract:

Planning a precision grasp for a robot hand is usually decomposed into two main steps. First, a set of contact points over the object surface must be determined, ensuring they allow a stable grasp. Second, the inverse kinematics of the robot hand must be solved to verify whether the contact points can actually be reached. Whereas the first problem has been largely solved in a general posing, the second one has only been tackled with local convergence methods. These methods only provide one solution to the problem, even if many are possible, and depending on the initial estimation they use, they may fail to converge, which results in grasp re-planning in situations where it could be avoided. This paper overcomes both issues by providing a complete method to solve the kinematics of human-like hands. The method is able to find all possible configurations that reach the specified contact points, even when positive-dimensional sets of such configurations are possible.

Links: ICRA08 paper

60 Seminar 28-2-2008

Speaker: Lluís Ros ()
Title: Position analysis of multi-loop robots and molecules
Abstract:

This talk presents a numerical method for the position analysis of multi-loop robots and molecules. The method is general (it can be applied to single or multiple interconnected loops of arbitrary topology) and complete (it isolates all solutions, even if they form positive-dimensional sets). Generality is achieved by reducing the problem to finding all embeddings of a set of points constrained by pairwise distances, which can be formulated as computing the roots of a system of Cayley-Menger determinants. Completeness is achieved by expressing these determinants in Bernstein form and using a numerical algorithm that exploits such form to bound all root locations at any desired precision. The method is readily parallelizable, and the current implementation can be run on single- or multi-processor machines. Experiments are included that show the method's performance on rigid loops, mobile loops, and multi-loop molecules. In all cases, complete maps including all possible configurations are obtained, thus allowing an exhaustive analysis and visualization of all pseudo-rotation paths between different conformations satisfying loop closure.

Links: JCC paper, Cuik II on Biomolecules

59 Seminar 13-12-2007

Speaker: José L. Albarral ()
Title: Natural Landmark Detection for Visually-Guided Robot Navigation
Abstract:

The main difficulty to attain fully autonomous robot navigation outdoors is the fast detection of reliable visual references, and their subsequent characterization as landmarks for immediate and unambiguous recognition. Aimed at speed, our strategy has been to track salient regions along image streams by just performing on-line pixel sampling. Persistent regions are considered good candidates for landmarks, which are then characterized by a set of subregions with given color and normalized shape. They are stored in a database for posterior recognition during the navigation process. Some experimental results showing landmark-based navigation of the legged robot Lauron III in an outdoor setting are provided.

Links:

58 Seminar 22-11-2007

Speaker: Júlia Borràs ()
Title: Flagged Parallel Manipulators
Abstract:

Flagged parallel manipulators are a special kind of parallel robots with nice kinematics and singularity properties. This seminar will first do an overview of parallel robots and the kinematic problems associated, talking about several mathematical tools we use to model them. Next, the study of flagged robots and their nice properties will be presented, as well the stratification of their singularity loci and geometric methods to generate a family of manipulators with the same properties.

Links:

57 Seminar 25-10-2007

Speaker: James L. Crowley
Title: Perception of Human Activity for Augmented Environments
Location: Exceptionally, this seminar will be at the FME assembly hall at 10:00 and not at the IRI meeting room at 12:00.
Abstract:

In this presentation I will discuss the problem of augmenting ordinary objects and environments with information and communication technologies. I will begin with a presentation of projected interaction in the PRIMA Augmented Meeting Environment at INRIA Rhones-Alpes. I will present video demonstrations in which ordinary surfaces are augmented with abilities for presentation and interaction using a steerable camera-projector pair. I will then address the looming problem of disruption created by the large-scale use of such technologies. I present a possible solution to this problem that relies on providing environments with an understanding of the activity and context of activities. I will briefly describe a layered architecture for building context aware services for augmented environments, finishing with an example of an automated video acquisition systems for context aware recording and transmission of meetings and lectures.

Links: Home page

56 Seminar 11-10-2007

Speaker: Babette Dellen ()
Title: Extraction of region correspondences via an n-d conjoint spin relaxation process driving synchronous segmentation
of image sequences
Abstract:

In this talk I will present a novel algorithm for the computation of corresponding image regions of stereo pairs and motion sequences. In this framework, local correspondences synchronize gray-level segmentation of the image via an n-d conjoint spin relaxation process, which is based on the method of superparamagnetic clustering of data. Spin interactions result in the formation of clusters of correlated spins, providing an automatic labeling of corresponding image regions. I will show results for both synthetic and real image sequences, and suggest potential applications of the algorithm.

Links:

55 Seminar 27-9-2007

Speaker: Viorela Ila ()
Title: Outdoor Delayed-State Visually Augmented Odometry
Abstract:

This talk presents an efficient approach to outdoor visually augmented odometry. The technique computes relative pose constraints via a robust least squares minimisation of 3D point correspondences, which are in turn obtained from the matching of SIFT features over two consecutive image pairs. Pose constraints are then used to build a history of pose estimates with and incremental delayed-state information filter. The efficiency of the approach resides on the exact sparseness of the delayed-state information form used. We also propose a loop closure test in a SLAM context that checks both for closeness of means and for highly informative updates at the same time.

Links:

54 Seminar 14-6-2007

Speaker: Josep M. Mirats ()
Title: Tensegrities
Abstract:

Us parlarà de tensegrities, què són, perqué s'aguanten a l'espai, els diferents punts de vista des d'els quals els podem analitzar per obtenir les seves propietats de rigidés i estabilitat: moviments dels nodes, forces que suporten els membres, energia de l'estructura. En aquest repàs veurem l'estat de l'art, les aplicacions existents, el que hem après d'aquestes estructures i el que ens falta per aprendre. Després revisarem el que estem fent el grup de persones que hi treballem, així com els objectius dels dos projectes que actualment hi ha en tensegrity.

Links:

53 Seminar 7-6-2007

Speaker: Josep M Font ()
Title: Dynamic mobile robot positioning from angular measurements using a two-step algorithm based on Kalman filtering
and triangulation
Abstract:

The presented work focuses on mobile robot dynamic localization based on the angular measurements of the orientation, relative to the robot frame, of the straight lines between one of the robot points and artificial landmarks. The measurements are assumed to be done by means of a rotating laser scanner that detects the different landmarks. It is well-known that under robot static condition, geometric triangulation methods can be used to determine the robot pose from the measured angles.
However, when the robot moves the landmarks are detected at different vehicle configurations and therefore the geometric methods cannot be consistently used to determine the robot pose directly from the measurements. This problem, known in robotics as the dynamic positioning problem, has been widely studied in the past decades and solved by many authors using a pose-state Kalman filter. This algorithm fuses the robot positional odometry with each of the laser measurements to estimate the robot pose. In this work, an angular-state vector is considered for the Kalman filter in order to estimate the landmark angles. By using this algorithm, the evolution of each landmark angle between actual laser measurements is obtained, and then the triangulation methods can be consistently used at any time to calculate the robot pose.
The work presents computer simulations and real experiments using the robot SPHERIK-3x3, which has an omnidirectional kinematics (with 3 DOF) and is based on three directionally sliding wheels. The results obtained both in the simulations and the experiments, show that the developed method performs better in terms of positioning accuracy.

Links: Web page

52 Seminar 31-5-2007

Speaker: Andreu Corominas ()
Title: Active and Cooperative Map-based Global Localization for Mobile Robots in Large Environments
Abstract:

In this talk an active and cooperative strategy to solve the map-based global localization problem for autonomous mobile robots is presented. Starting from a completely lost robot, common strategies such as particle filtering or place recognition result in a finite number of position hypotheses, specially in cases of large and real environments. For a fully autonomous system, the robot has to decide moving somewhere in order to disambiguate this set of hypotheses. Usually, places within the robot sensor horizons can not disambiguate the set of hypotheses, therefore a technique searching beyond them is required. This work presents a probabilistic strategy that uses the map of the environment to evaluate candidate positions in an exploration area around the robot. The strategy is neither sensor dependent nor spatial representation dependent and is easily extensible to multi-robot and sensor network contexts. Finally, an implementation of the proposed strategy is discussed and some simulation results in a real environment of 10000 m2 are shown validating the presented approach.

Links:

51 Seminar 17-5-2007

Speaker: Leonel Rozo ()
Title: Haptic Devices and Applications
Abstract:

Haptics devices have had a big evolution in the last ten years and the number of different applications in Robotics has increased hugely. This has caused many researchers to be concerned with the development of better tactile and force feedback devices for supporting all areas where this kind of machines could be useful. The goals of this seminar are to review the basic concepts about haptics, to show the different kinds of haptic devices and their main features. The Delta Haptic Device recently purchased by the institute will be introduced. On other hand, the most important applications in Robotics will be explained. Current projects about haptic rendering in virtual environments and haptic feedback for Teleoperation tasks will be discussed in depth.

Links:

50 Seminar 10-5-2007

Speaker: Juan A. Carretero ()
Title: Parallel manipulator kinematics and other robotics-related research at the RaM Lab
Abstract:

This presentation will serve as an overview of the research activities conducted at the Robotics and Mechanisms Laboratory (RaM Lab) at the University of New Brunswick (Canada). Current research work mainly concentrates on two general areas: the study of parallel manipulators and the simulation of robotic systems.
In the area or parallel manipulators, studies are being conducted on kinematic and actuation redundancy in order to improve workspace characteristics (e.g., workspace size and quality, force capabilities). Additionally, studies involving the development of new kinematic calibration methods for reduced degree-of-freedom parallel manipulators are also under way. The new methods are being validated through a test platform based on the 3-PRS parallel architecture.
In terms of the computer simulation of robotic systems, the current work focuses on the development of a simulation environment for tethered underwater vehicles. More particularly, new methods for determining the separation distance and contact status between convex and/or concave objects are currently being extended for flexible slender objects (e.g., ROV tether). Contact dynamics models for such objects have also been developed and are being incorporated into realistic tethered vehicle simulators.

Links: Web page

49 Seminar 3-5-2007

Speaker: Vicente Ruiz de Angulo ()
Title: Exploiting single-cycle symmetries in Branch-and-Prune algorithms
Abstract:

Symmetries in discrete constraint satisfaction problems have been explored and exploited in the last years, but symmetries in continuous constraint problems have not received the same attention. Here we focus on permutations of the variables consisting of one single cycle. We propose a procedure that takes advantage of these symmetries by interacting with a Branch-and-Prune algorithm without interfering with it. A key concept in this procedure are the classes of symmetric boxes formed by bisecting a n-dimensional cube at the same point in all dimensions at the same time. We analyze these classes and quantify them as a function of the cube dimensionality. Moreover, we propose a simple algorithm to generate the representatives of all these classes for any number of variables at very high rates. A problem example from the chemical field and a kinematics solver are used to show the performance of the approach in practice.

Links:

48 Seminar 19-4-2007

Speaker: Guillem Alenyà ()
Title: Depth from the visual motion of a planar target induced by zooming
Abstract:

Robot egomotion can be estimated from an acquired video stream up to the scale of the scene. To remove this uncertainty (and obtain true egomotion), a distance within the scene needs to be known. If no a priori knowledge on the scene is assumed, the usual solution is to derive ``in some way" the initial distance from the camera to a target object. We propose a new, very simple way to obtain such a distance, when a zooming camera is available and there is a planar target in the scene. Similarly to ``two-grid calibration" algorithms, no estimation of the camera parameters is required, and no assumption on the optical axis stability between the different focal lengths is needed. Quite the reverse, the non stability of the optical axis between the different focal lengths is the key ingredient that enables to derive our depth estimate, by applying a result in projective geometry. Experiments carried out on a mobile robot platform show the promise of the approach.

Links:

47 Seminar 22-3-2007 (Cancelled by the speaker)

Speaker: Federico Thomas ()
Title:
Abstract:
Links:

46 Seminar 8-3-2007

Speaker: Juan Andrade-Cetto ()
Title: A Wire-based Active Tracker
Abstract:

Wire-based tracking devices are an affordable alternative to costly tracking devices. They consist of a fixed base and a platform, attached to the moving object, connected by six wires whose tension is maintained along the tracked trajectory. One important shortcoming of this kind of devices is that they are forced to operate in reduced workspaces so as to avoid singular configurations. Singularities can be eliminated by adding more wires but this causes more wire interferences, and a higher force exerted on the moving object by the measuring device itself. In this talk we show how, by introducing a rotating base, the number of wires can be reduced to three, and singularities can be avoided by using an active sensing strategy. This also permits reducing wire interference problems and the pulling force exerted by the device.

Links:

45 Seminar 28-2-2007

Speaker: Markus Klettner / José Antonio Casas
Title: Our Answer to NASA's Beam Power Challenge
Abstract:

The Spaceward Foundation manages NASA's annual beam power challenge that will be taking place during the X Prize Cup in New Mexico. Being the first contest of NASA's X-Prize inspired Centennial Challenges Program the beam power challenge requires to design, build and operate a climber, a machine just powered by a beam of light that can drive up and down a tether ribbon, while carrying a payload.
Entries are rated according to the product of payload weight and climbing speed, normalized by the net climber weight. This metric is representative of the throughput of a space transportation system based on the Space Elevator.
Our prototype climber will be composed by a panel of photovoltaic power receptors that power a climb motor, all this assembled in a lightweight aluminium structure. The traction mechanism is formed by wheels that gripe the ribbon. The climber will need some power conditioning and control circuits. These circuits control the climber speed and adjust the voltage and current supplied by the solar cells to the traction motor and check the bottom and top of the ribbon.
A very important part is the beam source system. This source has to be concentrated and to be similar to the solar light, because the solar cells that we will be employing are designed for this type of radiation. Hence we will be using a Xenon bulb that matches sun light.
The climber design includes a super capacitor that will be storing part of the energy supplied by the solar panel. The stored energy will allow to keep climbing the ribbon in a steady and continuous manner, even without light for a few seconds. In addition we are planning to use a Fresnel lens to be able to better focus the beam of light at an increased distance from the Xenon source, leading to improved power supply by the solar array at higher altitudes of the climber.
At the moment the project is in its initial phase. We have got a prime sponsor and have started assembling the basic parts of the prototype.

Links: Introduction, Web page

44 Seminar 7-2-2007

Speaker: Ales Ude ()
Title: Learning object representations by manipulation
Abstract:

In the first part of my talk I will briefly present our previous work on transferring human movements to humanoid robot movements, learning from observations using primitives, and on foveated vision and object recognition. In the second part I shall present our current work on building object representations by manipulation. I will focus on how to exploit a humanoid robot's motor capabilities to learn representations of objects without having any prior information about them. We will show that by taking control of the object the humanoid can resolve the problems arising in systems that rely on purely bottom-up attention and segmentation, which enables the robot to acquire appearance models suitable for recognition.

Links: Home page

43 Seminar 25-1-2007

Speaker: Sergi Hernàndez ()
Title: Tensegrity form-finding method based on non-linear programming
Abstract:

Tensegrity structures are pre-stressable truss-like systems which mantain their form due to an intrincate balance of forces between a disjoint set of rigid elements (bars) and a continuous set of tensile elements (cables). One of the fundamental problems of such structures is, given a description of the elements of the structure (cables or bars) and their topological relationship, find a stable configuration. This process implies both geometric and static constraints, and has received a lot of attention since this kind of structures were introduced. In this talk we propose a form-finding method based on non-linear programming which takes into account both geometric and static constraints and is shown to converge always to a valid solution (if it exists). The problem is solved in a higher dimensional space to speed up the convergence.

Links: Slides

42 Seminar 14-12-2006

Speaker: Carlos Albores ()
Title: Accurate position uncertainty estimation and propagation for Autonomous Vehicles
Abstract:

When a robot has to solve a task in a unconstrained general environment it is required to know both, its position, and the uncertainty for this position estimate. It is usual to express this uncertainty by means of a covariance matrix. In a previous work we presented a method to obtain a close form expression for the uncertainty in the odometry position estimate of a mobile vehicle using a covariance matrix whose form is derived from the kinematic model. We then particularize for a non-holonomic Ackerman driving type autonomous vehicle. Its kinematic model relates the two measures being obtained for internal sensors: the velocity, translated into the instantaneous displacement, and the instantaneous steering angle. However, obtaining a close form expression for the cross-covariance terms between the previous position of the robot and its actual increment of position is not straight forward. Thus, a formulation to obtain a close form expression for these terms is developed. The basic idea of the method herein presented is to fit the covariance matrix for the previous position using a set of equations that, at the same time, simplifies its mathematical expression. A Monte Carlo simulation is performed in order to compare the obtained position uncertainty with the presented method and others used to estimate and propagate the errors, such as the classical Jacobian and the scaled unscented transformation.

Links:

41 Seminar 23-11-2006

Speaker: Erika Ottaviano ()
Title: LARM Research Activities on Cable-Based Parallel Manipulators
Abstract:

In this presentation research activities are discussed, which have been developed at LARM: Laboratory of Robotics and Mechatronics, University of Cassino. After a brief introduction on teaching and research issues developed at LARM, specific attention is addressed to both passive and active cable-based parallel manipulators. In particular, passive cable robots can refer to measuring systems, while active cable-based parallel manipulators are actively actuated. Prototypes and experiences are described as concerning with CATRASYS (Cassino Tracking System), a pose estimation device, and CALOWI (Cassino Low-Cost easy-Operation Wire Robot), a 4-4 cable-suspended manipulator. Further applications are discussed together with those referring to current collaboration between LARM and IRI.

Links: LARM web page

40 Seminar 9-11-2006

Speaker: Oscar Serra ()
Title: Hierarchical Models for Object Recognition
Abstract:

Although the original idea is not new (Neocognitron - Fukushima - 1991), recent neurophysiological findings in the visual cortex have helped to come up with a model capable of interesting invariant capabilities, that competes with some of the best state-of-the-art computer vision systems. The so called standard model was fistly implemented in the MIT (Serre, Wolf and Poggio - 2005) and recently improved (August 2006). The Hierarchical Model progressively processes the visual information through several levels, that need the output of the previous ones to proceed, and are organized hierarchically. It is still unclear, however, which bio-inspired principles are best in computer vision systems; so the objective of the talk will be to present a basic model and to discuss possible changes.

Links:

39 Seminar 26-10-2006

Speaker: Jorge Scandaliaris ()
Title: Color Edges and Photometric Invariants
Abstract:

I will expose the ideas behind a couple of works where photometric invariants are used to detect or classify color edges. The first work uses two interesting ideas to classify the physical nature of edges: an automatic noise-adaptive local thresholding; and a taxonomy on color-edge types based on the properties of different color models. The second work proposes a set of quasi-invariants based on derivatives that have good discriminative power and are robust againts noise.

Links:

38 Seminar 5-10-2006

Speaker: Enric Celaya ()
Title: Interval propagation for the determination of feasible linkage configurations
Abstract:

In this talk I will present an interval propagation algorithm for variables in planar and spherical single-loop linkages. Given an interval constraining the allowed values for an input variable, the propagation algorithm finds the set of values for an output variable for which there is at least one solution of the loop equation satisfying the constraint. A recent extension of the propagation algorithm allows the propagation of multiple constraints imposed on different variables at the same time. This provides a powerful tool to find the solution sets of complex multi-loop linkages by iterative methods.

Links:

37 Seminar 21-9-2006

Speaker: Josep M. Porta ()
Title: Multi-loop Position Analysis via Iterated Linear Programming
Abstract:

In this talk we will present a numerical method able to isolate all configurations that an arbitrary loop linkage can adopt, within given ranges for its degrees of freedom. The procedure is general, in the sense that it can be applied to single or multiple intermingled loops of arbitrary topology, and complete, in the sense that all possible solutions get accurately bounded, irrespectively of whether the analyzed linkage is rigid or mobile. The problem is tackled by formulating a system of linear, parabolic, and hyperbolic equations, which is here solved by a new strategy exploiting its structure. The method is conceptually simple, geometric in nature, and easy to implement, yet it provides solutions at the desired accuracy in short computation times.

Links: RSS06 paper

36 Seminar 14-7-2006

Speaker: Teresa Vidal ()
Title: Observability Analysis of Bearing-Only SLAM Systems
Abstract:

In this talk we present an observability analysis for a number of single camera SLAM systems. The aim is to get a better understanding of the well known intuitive behaviour of these systems, such as the need for triangulation to features from different positions in order to get accurate relative pose estimates. The characterization of the unobservable directions is made using the nullspace basis of the stripped observability matrix. This allow us to identify the type of inputs that should be avoided in order not to incur in unobservable conditions. The analysis is performed modeling the systems in the continous time domain as piecewise linear.

Links:

35 Seminar 30-6-2006 (Cancelled by the speaker)

Speaker: Alberto Sanfeliu ()
Title:
Abstract:
Links:

34 Seminar 22-6-2006

Speaker: José del Rocio Millan ()
Title: Brain-Controlled Robots
Abstract:

The idea of moving robots or prosthetic devices not by manual control, but by mere thinking (i.e., the brain activity of human subjects) has fascinated researchers for the last 30 years, but it is only now that first experiments have shown the possibility to do so. Such a kind of brain-computer interface (BCI) is a natural way to augment human capabilities by providing a new interaction link with the outside world and is particularly relevant as an aid for physically disabled people. In this talk I will review the field of BCI, with a focus on how brainwaves can be used to directly control robots. Most of the hope for such a possibility comes from invasive approaches that provide detailed single neuron activity; however, it requires surgical implantation of microelectrodes in the brain. For humans, non-invasive systems based on electroencephalogram (EEG) signals are preferable but, until now, have been considered too poor and slow for controlling rapid and complex sequences of movements. Recently we have shown for the first time that online analysis of a few EEG channels, if used in combination with advanced robotics and machine learning techniques, is sufficient for humans to continuously control a mobile robot.

Links:

33 Seminar 15-6-2006

Speaker: Guillem Alenyà ()
Title: Anàlisi estadístic de la propagació de l'error en estimar el moviment 3D a partir d'imatges
Abstract:

S'analitza la precisió d'un métode per estimar com es belluga una cámera a partir de les imatges que va captant. D'una vista a una altra es produeix una transformació afí del pla de la imatge caracteritzada per 6 parámetres, a partir dels quals es calcula la translació i la rotació de la cámera. Mitjançant simulacions de Monte Carlo es valida l'aplicabilitat d'un procediment estadístic menys costós, la Unscented Transformation, que permet obtenir les mitjanes i covariances del moviment 3D aplicant el mètode només a 13 mostres per vista. Els resultats indiquen que les translacions i la rotació en el pla de la imatge es recuperen millor que la translació segons l'eix òptic, que a la seva vegada supera les dues rotacions fora del pla. Quant a les covariances, només apareix un cert lligam entre els tres graus de llibertat menys precisos.

Links:

32 Seminar 25-5-2006

Speaker: Jordi Cornella ()
Title: Geometrical Approaches for Optimal Grasps Synthesis
Abstract:

Grasping and manipulation of objects are fundamental tasks in the use of robots in industrial processes as well as in robotized applications in non-structures environments. The development of versatile end-effectors, like the mechanical hands, has the intention of widening the field of robot applications as well as the efficiency of the robots, but, in contrast, the kinematics and control complexity of these devices and the constraints on the grasping action make it necessary the use of systems that automatically solve the different problems associated with a grasping operation. This talk presents some approaches developed in my thesis to solve two problems involved in grasping and manipulation of objects by means of mechanical hands: the determination of the contact points between the fingers and the object that assure the object immobility, and the determination of the contact forces that each finger has to exert in order to maintain the object equilibrium. In general, these problems can have multiple solutions, some of them being more adequate than others for a given purpose. Then, the objective of the proposed approaches is the determination of the final solution taking into account quality criteria, namely, the robustness of the grasp in front of external perturbations forces and finger positioning errors in the determination of the contact points, and the minimization of the contact forces.

Links: IOC web page

31 Seminar 10-5-2006

Speaker: Alberto Sanfeliu (), Federico Thomas (), Carme Torras ()
Title: Strep projects seminar
Abstract:

This special seminar will be a brief introduction to the three STREP project proposals in which the IRI robotics group is involved. The three STREP proposlas are:

  • URUS: Ubiquitous Robotics in Urban Setting (Alberto Sanfeliu)

  • MARIONET: Wire-driven parallel robot for human being servicing (Federico Thomas)

  • AIR: Advanced Interactive Robotics (Carme Torras)

Links:

30 Seminar 4-5-2006

Speaker: Florentin Woergoetter ()
Title: Temporal Sequence Learning in Neurons and Robots
Abstract:

Biological agents can in a very flexbile way adapt to their environment and this is achieved by changing the properties of large networks of neurons through synaptic plasticity following learning. Principles of synaptic plasticity have been used in technical fields in conjunction with the design of artificial neural networks (ANNs), but examples where neural networks are actually controlling complex robots are still quite rare. The conflict between the required reliablility in robot control and the more fuzzy and distributed responses of neuronal controllers makes it hard to use ANNs for achieving robust behavior. Here we show that one can design a synaptic learning rule at the single unit level, implement this into individual neurons, which already control simple actions, and finally design a more complex 24-unit neuronal controller to generate robust and adaptable gaits in a planar biped walking robot.

Links:

29 Seminar 20-4-2006

Speaker: Tom Creemers ()
Title: Computation of Planar Linkage Configurations - Algorithm & Software Architecture
Abstract:

In this talk we will introduce a suite of algorithms to deal with planar linkage configuration spaces. The kernel of this suite is a numerical method able to compute all possible configurations of a planar linkage. The procedure is applicable to rigid linkages (i.e., those that can only adopt a finite number of isolated configurations) and to mobile ones (i.e., those that have internal degrees of freedom). The method is based on the fact that this analysis always reduces to finding the roots of a polynomial system of linear, quadratic, and hyperbolic equations, which is here tackled with a new strategy exploiting its structure. The method is conceptually simple, geometric in nature, and easy to implement, yet it provides solutions of the desired accuracy in short computation times. Experiments are included which show its performance on the double butterfly linkage, for which an accurate an complete discretization of its configuration space is obtained.

Links: ICRA06

28 Seminar 30-3-2006 (Cancelled by the speaker)

Speaker: Jorge Scandaliaris ()
Title:
Abstract:
Links:

27 Seminar 23-3-2006

Speaker: Viorela Ila ()
Title: VLSI Architecture for Motion Estimation in Underwater Imaging
Abstract:

This seminar introduces the main topics developed in my thesis integrating knowledge from several fields such as underwater robotics, computer vision, VLSI architectures and reconfigurable devices. Underwater robotics was the motivation of this work, even though computer vision and parallel VLSI architectures played the most important role. Many technologies exist to provide information about a vehicle position, but among them vision based systems represent a good option due to their low cost, high-rate and high-resolution. From the relative observation of the seafloor, a vision system can estimate the vehicle's motion. While moving, the robot can build a map of the seafloor. These visual maps are known as mosaics and can be used for the localisation of the vehicle. The apparent motion of a camera mounted on an underwater vehicle can be estimated by correlating two successive frames of an image sequence. However, searching for correspondences is a time-consuming and error-prone process. Lack of well-defined contours caused by blurring of the elements of the image, as well as non-uniform illumination when using artificial light makes underwater scenes much more difficult to be processed than normal images. Therefore, methods frequently used in standard image processing must be modified and adapted to these particular conditions. We proposed in this thesis a method based on texture characterisation of points to reject outliers from the image correspondence problem. On the other hand, a parallel implementation was used to speed-up parts of the motion estimation algorithm which have a computationally high load. A new VLSI architecture is proposed with the aim of achieving frame-rate performance.

Links: Home Page

26 Seminar 16-3-2006

Speaker: José L. Albarral ()
Title: Color Segmentation of Natural Landmarks for Outdoor Robotic Vision
Abstract:

Our research pursuits the autonomous navigation of mobile robots in outdoor environments. In these environments the robot is going to rely on the detection, characterization and later recognition of natural landmarks for orientation and localization purposes. In this context, a color based image segmentation algorithm has been designed to extract the most relevant regions composing a natural landmark. Then, the spatial moments and the topological relations of the extracted regions are used together with their color information for the characterization of the landmark. Our image segmentation strategy combines the color discrimination capabilities of histograms with the spatial capabilities of region growing methods and works in the HSI color space for its robustness to changing illumination conditions. But the color discrimination is not uniform in any of its hue, saturation or intensity domains. Therefore, we have modelled those heterogeneous discrimination properties in order to obtain a uniform color distance measure that provides proper segmentation results. The result is a fast and robust color segmentation algorithm suitable for our robotic navigation system.

Links:

25 Seminar 23-2-2006

Speaker: Alejandro Agostini ()
Title: Reinforcement learning for complex tasks with efficient generalization
Abstract:

In applications where an agent is required to develop a complex task it is hard to code which action must be executed in each situation. It is simpler to make the agent learn to execute the task autonomously, interacting with the environment and learning by the experience. This learning paradigm is denoted as reinforcement learning (RL). The main difficulty of RL is the vast number of different situations the agent should experience in a complex task, making the straight application of this paradigm impracticable in many problems. To make it feasible the agent must be supplied with the capability to generalize from the experienced situations in order to infer which action to execute in those situations never perceived. The same generalization problem is raised in many fields of AI, where some inference is required in problems where the amount of information is computationally intractable. Therefore, many of the generalization techniques applied in RL are the same as those applied in other learning paradigms, mainly in supervised learning. These techniques still have problems when generalizing in complex tasks, constituting an open research area. Our work consists in finding a generalization technique that improves the results obtained so far, and develop a method that combines this technique with RL for an agent to learn complex tasks in real environments. One of the main assumptions in our research is that each variable the agent must perceive to complete a task has different relevancies for the inference depending on the situation, where a situation not only consists of perceptions but also implicates actions. Current generalization techniques assume that a variable has the same relevance for all the situations the agent could face wasting opportunities of generalization. The most relevant perceived variables for a situation, with the coupled actions, are gathered in a rule. In order to create a structure that supports the rules system, where some variables should be considered for some situations and not for others, we developed a function representation that consists in overlapped subspaces of the domain. Each subspace has assigned a value of the represented function (in our case, the cumulative reward q). Given a particular instance (a situation) many overlapped subspaces are involved and only one of them determines the value of the function. This is done after a competition among the involved subspaces where the subspace that wins determines the value of the function. The developed technique progressively builds and embeds rules in the mentioned representation, solving the arising representation inconsistencies while learning the value function. These rules finally contain the relevant information to optimally complete the task. The developed technique so far finds a consistent function representation, deals with the inherent uncertainties of the real environment by using estimation theory, and is robust to nonstationarities of the system as required by the RL.

Links:

24 Seminar 9-2-2006

Speaker: Josep M Porta ()
Title: Robot Planning in Partially Observable Continuous Domains
Abstract:

In this talk I will describe a value iteration algorithm for learning to act in Partially Observable Markov Decision Processes (POMDPs) with continuous state spaces. Mainstream POMDP research focuses on the discrete case and this complicates its application to, e.g., robotic problems that are naturally modeled using continuous state spaces. The main difficulty in defining a (belief-based) POMDP in a continuous state space is that expected values over states must be defined using integrals that, in general, cannot be computed in closed form. We will first see that the optimal finite-horizon value function over the continuous infinite-dimensional POMDP belief space is piecewise linear and convex, and is defined by a finite set of supporting functions that are analogous to the vectors (hyperplanes) defining the value function of a discrete-state POMDP. Second, we will see that, for a fairly general class of POMDP models in which all functions of interest are modeled by Gaussian mixtures, all belief updates and value iteration backups can be carried out analytically and exact. A crucial difference with respect to the vectors of the discrete case is that, in the continuous case, the functions will typically grow in complexity (e.g., in the number of components) in each value iteration. Finally, I will describe the application of Perseus, a randomized point-based value iteration algorithm, in a simple robot planning problem with a continuous domain.

Links: RSS'05 paper

23 Seminar 15-12-2005 (Cancelled by the speaker)

Speaker: Oscar Serra ()
Title:
Abstract:
Links:

22 Seminar 1-12-2005

Speaker: Carme Torras ()
Title: Robots adaptatius: projectes passats i futurs
Abstract:

La presentació constarà de dues parts: primer descriuré alguns dels resultats obtinguts a l'IRI en l'aplicació de xarxes neuronals a l'aprenentatge de la cinemàtica i la coordinació visuomotora de robots, i després esbossaré les línies directrius del projecte "Perception, Action and Cognition through Learning of Object-Action Complexes", aprovat dins del 6è Programa Marc de la Comissió Europea i que començarà el proper febrer.

Links: Related paper

21 Seminar 24-11-2005

Speaker: Teresa Vidal ()
Title: Active Control for Single Camera SLAM
Abstract:

We consider a single hand-held camera performing SLAM at video rate with generic 6DOF motion. The aim is to optimise both the localisation of the sensor and building of the feature map by computing the most appropriate control actions or movements. The actions belong to a discrete set (e.g. go forward, go left, go up, turn right, etc), and are chosen so as to maximise the mutual information gain between posterior states and measurements. Maximising the mutual information helps the camera avoid making ill-conditioned measurements appropriate to bearing-only SLAM. Moreover, orientation changes are determined by maximising the trace of the Fisher Information Matrix. In this way, we allow the camera to continue looking at those landmarks with large uncertainty, but from betterposed directions. Various position and gaze control strategies are first tested in a simulated environment, and then validated in a video-rate implementation. Given that our system is capable of producing motion commands for a real-time 6DOF visual SLAM, it could be used with any type of mobile platform, without the need of other sensors.

Links:

20 Seminar 17-11-2005

Speaker: Pablo Riera ()
Title: A MatLab Toolbox for Solving the Kinematics of Planar Mechanisms
Abstract:

We have developed a suite of MatLab scripts for solving the Input/Output problem of planar mechanisms. The mechanism is described in a matrix that is given to the toolbox as input. The toolbox extracts the loop-closure equations and finds all isolated roots of the equation system by polynomial continuation homotopy. Then all the possible configurations of the mechanisms are drawn.

Links:

19 Seminar 10-11-2005

Speaker: Michael Villamizar ()
Title: An Object Detection System
Abstract:

We will describe a system to detect objects on images that is invariant to translations, scaling and rotations of the object, moreover it presents a low computational cost and high detection rates. The system is based on local features to detect contours that are combined with the AdaBoost Algorithm to obtain a robust classifier able to detect the object in different contexts.

Links:

18 Seminar 4-11-2005 (at 10:30)

Speaker: Nikolay Ivanov ()
Title: One Approach to the Development of Parallel Computing Machine
Abstract:

This work discusses some aspects of the process of developing of parallel computing machine. The mathematical concept "algebra" is used as a basis of the investigation,. The application of this notion allows the representation of any task of this type as an ordered structure of three components: set of elements (Xilinx's FPGA), set of rules for action (concrete, particular algorithm), and set of constraints. It is shown that such an approach cover in practice the complete set of all valuable varieties of the problem and permits to find the optimal solution for every particular task.

Links:

17 Seminar 2-11-2005 (at 10:30)

(We have two visitors from the Institute of Information Technologies, Bulgarian Academy of Science
that will give us two talks, one on Wednesday and one on Friday
)
Speaker: Georgi Gluhchev ()
Title: Automatic Line Detection
Abstract:

An approach is described aimed at the detection of lines in digital images and evaluation of metric parameters of the objects. It is based on the properties of the Fourier Transform which allow accumulating energy of pixels lying on straight lines of same slope alongside a single straight line in the frequency domain. Using only the spectrum values alongside the latter line permits the detection of straight line segments in the original image and the evaluation of specific parameters. This technique also allows detecting characteristic points like corner points, points of intersection or bifurcation, thus describing the original image as a set of geometric features. The approach could be applied to the problems of document processing, robot vision and like.

Links:

16 Seminar 20-10-2005

Speaker: Gorka Bonals ()
Title: A probabilistic path planner for two 6R manipulators simultaneously grasping an object
Abstract:

We present a probabilistic path planning method which computes collision-free paths for two robotic arms Staübli RX60 that simultaneously grasp an object. The method assumes that the environment is static and that accurate geometric models of their objects are known. It proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collision-free configurations and whose edges correspond to feasible paths between these configurations. These paths are computed using a simple and fast local planner. In the query phase, any given start and goal configurations of the two robotic arms are connected to two nodes of the roadmap, and the roadmap is then searched for a path joining them. The method is an adaptation of the well-known algorithm by Svetska et al. , which has been appropriately modified so as to take into account the closure constraint imposed by the simultaneous grasp of the object.

Links:

15 Extra Seminar 13-10-2005

Speaker: Joan Solà, visitor from LAAS ()
Title: Delayed and Undelayed methods for Bearing Only SLAM
Abstract:

Most solutions to the SLAM problem in robotics have utilized Range and Bearing sensors as the provided perception data is easy to incorporate, allowing immediate landmark initialization. This is not the case when using Bearing-Only information because the distance to the perceived landmarks is not directly provided. A whole estimate of a landmark position will only be possible via a set of measurements taken from different points of view.

The vast majority of contributions to this problem perform a parallel task to get this estimate, and hence the landmark initialization is delayed. However, a second family of methods exist that map the bearing information of the landmark without the need of knowing its distance. Pros and cons of both delayed and undelayed families will be discussed, as well as today's most performing algorithms for each one of them in the EKF-SLAM framework.

Pros and cons for vision-based SLAM systems can be resumed as follows: a) delayed methods are more optimal than undelayed ones, in the senses of information loss and consistency; b) only undelayed methods can be used in vehicles that have the vision sensors looking in the motion direction; and c) delayed methods can more easily deal with unstable or short living detected visual features.

The boost in algorithm performance is mainly based on the following hints: a) non-Gaussian PDFs of the unobserved distance to the landmarks are approximated by Gaussian mixtures instead of sets of particles; b) these Gaussian mixtures are best defined as geometric series of Gaussians; c) for the delayed method, several landmarks are tracked at each frame and only the best ones will be initialized; and d) for the undelayed method, the Federated Information Sharing filter updating technique is developed and used. The consequence of these choices is the possibility to simultaneously initialize several landmarks in real time, with both the delayed and undelayed algorithms.

Some experimental results are given to show the pertinence of the proposed methods.

Links: IROS2005a, IROS2005b, Video

14 Seminar 6-10-2005

Speaker: Francesc Moreno ()
Title: Multiple Cue Integration for Robust Tracking in Dynamic Environments: Application to Video Relighting
Note: This seminar will be PhD thesis presentation rehearsal so expect it to be longer that usual seminars.
Consequently, this seminar will start at 11:00 instead of at 12:00.
Abstract:

Motion analysis and object tracking has been one of the principal focus of attention over the past two decades within the computer vision community. The interest of this research area lies in its wide range of applicability, extending from autonomous vehicle and robot navigation tasks, to entertainment and virtual reality applications.

Even though impressive results have been obtained in specific problems, object tracking is still an open problem, since available methods are prone to be sensitive to several artifacts and non-stationary environment conditions, such as unpredictable target movements, gradual or abrupt changes of illumination, proximity of similar objects or cluttered backgrounds. Multiple cue integration has been proved to enhance the robustness of the tracking algorithms in front of such disturbances. In recent years, due to the increasing power of the computers, there has been a significant interest in building complex tracking systems which simultaneously consider multiple cues. However, most of these algorithms are based on heuristics and ad-hoc rules formulated for specific applications, making impossible to extrapolate them to new environment conditions.

In this dissertation we propose a general probabilistic framework to integrate as many object features as necessary, permitting them to mutually interact in order to obtain a precise estimation of its state, and thus, a precise estimate of the target position. This framework is utilized to design a tracking algorithm, which is validated on several video sequences involving abrupt position and illumination changes, target camouflaging and non-rigid deformations. Among the utilized features to represent the target, it is important to point out the use of a robust parameterization of the target color in an object dependent colorspace which allows to distinguish the object from the background more clearly than other colorspaces commonly used in the literature.

In the last part of the dissertation, we design an approach for relighting static and moving scenes with unknown geometry. The relighting is performed through an `image-based' methodology, where the rendering under new lighting conditions is achieved by linear combinations of a set of pre-acquired reference images of the scene illuminated by known light patterns. Since the placement and brightness of the light sources composing such light patterns can be controlled, it is natural to ask: what is the optimal way to illuminate the scene to reduce the number of reference images that are needed? We show that the best way to light the scene (i.e., the way that minimizes the number of reference images) is not using a sequence of single, compact light sources as is most commonly done, but rather to use a sequence of lighting patterns as given by an object-dependent lighting basis. It is important to note that when relighting video sequences, consecutive images need to be aligned with respect to a common coordinate frame. However, since each frame is generated by a different light pattern illuminating the scene, abrupt illumination changes between consecutive reference images are produced. Under these circumstances, the tracking framework designed in this dissertation plays a central role. Finally, we present several relighting results on real video sequences of moving objects, moving faces, and scenes containing both. In each case, although a single video clip was captured, we are able to relight again and again, controlling the lighting direction, extent, and color.

Links:

13 Seminar 29-9-2005

Speaker: Pablo Jimenez ()
Title: Robots and Space Exploration (II)
Abstract:

The hostile conditions existing beyond the Earth's atmosphere convert space missions into one of the most well-founded applications of robots but also into one of the most challenging ones. A survey on the two big families of space robots is presented here: robotic arms for EVA operations (and their "natural" extension: humanoids), and rovers for planetary exploration (highly popular due the success of the last Mars missions). The talk will conclude with some new robotic concepts that cannot be classified into these categories.

Links:

12 Seminar 22-9-2005

Speaker: Pablo Jimenez ()
Title: Robots and Space Exploration (I)
Abstract:

The hostile conditions existing beyond the Earth's atmosphere convert space missions into one of the most well-founded applications of robots but also into one of the most challenging ones. A survey on the two big families of space robots is presented here: robotic arms for EVA operations (and their "natural" extension: humanoids), and rovers for planetary exploration (highly popular due the success of the last Mars missions). The talk will conclude with some new robotic concepts that cannot be classified into these categories.

Links:

11 Seminar 8-9-2005

Speaker: Sergi Hernàndez ()
Title: Unknown contour recovery using force and position feedback
Abstract:

In some applications, it is useful to retrieve the shape of a surface using a robot manipulator. Usually, this goal is achieved using the encoder data obtained while the robot is following the surface using a hybrid position/force or speed/force control. However, nonlinearities associated with the actuators such as backlash make impossible to get the real contour from the encoder data. To solve this problem, an external position feedback is added. This new feedback loop is based on a PSD (Position Sensing Device) sensor which presents some advantages over a traditional CCD camera such as speed an accuracy. Some experimental results are presented using a 3 dof manipulator following a 2D contour.

Links:

10 Seminar 21-7-2005 (Cancelled by the speaker)

Speaker: Jorge Scandaliaris ()
Title:
Abstract:
Links:

9 Seminar 7-7-2005

Speaker: Enric Celaya ()
Title: A flexible platform for vision-based robot navigation
Abstract:

A user interface for the control of vision-based robot navigation in previously unknown, indoor or outdoor environments will be described. The user, by means of a visual feedback from the camera(s) of the robot, can select a visual target to be reached by the robot and launch an autonomous navigation process. Manual control can be taken back by the user at any time. The interface is built as a modular platform, capable of accommodating different types of robots and different algorithms for vision and navigation. Thus, in addition to constitute a generic interface for navigation control, the platform can be used as a tool to test different navigation approaches and compare their performances in similar contexts.

Links:

8 Seminar 23-6-2005 (Delayed to 1-7-2005)

Speaker: Federico Thomas ()
Title: Moment-Preserving Image Approximations
Abstract:

An image can be seen as an element of a vector space and hence it can be expressed in as a linear combination of the elements of any non necessarily orthogonal basis of this space. After giving a matrix formulation of this well-known fact, this paper presents a reconstruction method of an image from its moments that sheds new light on this inverse problem. Two main contributions are presented: (a) the results using the standard approach based on the least squares approximation of the result using orthogonal polynomials can also be obtained using matrix pseudoinverses, which implies higher control on the numerical stability of the problem; and (b) it is possible to use basis functions in the reconstruction different from orthogonal polynomials, such as Fourier or Haar basis, allowing to introduce constraints relative to the bandwidth or the spatial resolution on the image to be reconstructed.

Links:

7 Seminar 9-6-2005

Speaker: Josep M Mirats ()
Title: A Closed Form Expression for the Uncertainty in Odometry Position Estimate of an Autonomous Vehicle
Abstract:

Using internal and external sensors to provide position estimates in a two-dimensional space is necessary to solve the localization and navigation problems for a robot or an autonomous vehicle. Usually, a unique source of position information is not enough so researchers try to fuse data from different sensors using several methods as for example Kalman filtering. Those methods need an estimation of the uncertainty in the position estimates obtained from the sensory system. This uncertainty is expressed by a covariance matrix, which is usually obtained from experimental data assuming, by the nature of this matrix, general and unconstrained motion. We propose in this paper a close form expression for the uncertainty in the odometry position estimate of a mobile vehicle using a covariance matrix whose form is derived from the cinematic model. We then particularize for a non-holonomic Ackerman driving type autonomous vehicle. Its cinematic model relates the two measures being obtained for internal sensors: the velocity, translated into the instantaneous displacement, and the instantaneous steering angle. The proposed method is validated experimentally, and compared against Kalman filtering.

Links:

6 Seminar 26-5-2005

Speaker: Lluís Ros ()
Title: Wrenchpad - A force sensitive tablet
Abstract:

This seminar presents a force and torque sensor developed within the project "Multi-arm roboticed cell" ("Estació de treball multibraçs"), funded by the CERTAP, a reference center of the Generalitat de Catalunya, the Catalan government. The sensor has the structure of a Stewart-Gough platform: it is made of two rigid plates, a base and a platform, linked by six legs, each mounting a single-axis load-cell to measure the force on it. We will see how the measures of the six legs can be combined to derive 1) the net wrench (force and torque) acting on the platform, 2) the line of application of the force, and 3) the uncertainties associated with all these magnitudes due to errors in the measured loads. The device is available in our lab for anyone wishing to play with it (just contact any of the authors).
The work presented in this seminar has been done in cooperation with Roger Frigola, Francesc Roure, and Federico Thomas.

Links: ICRA paper

5 Seminar 12-5-2005

Speaker: Guillem Alenyà ()
Title: Recovering Epipolar Direction from Two Affine Views of a Planar Object
Abstract:

Most approaches to camera motion estimation from image sequences require matching the projections of at least 4 non-coplanar points in the scene. The case of points lying on a plane has only recently been addressed, using mainly projective cameras. We here study what can be recovered from two uncalibrated views of a planar object under affine viewing conditions. Using results in projective geometry, we prove that the affine epipolar direction can be recovered provided camera motion is free of cyclorotation. This result is then confirmed analytically, and subjected to experimentation. A setup consisting of a Staubli robot holding a planar object in front of a camera is used to obtain calibrated image streams. The object contour is tracked and its affine deformation between any two frames extracted. The fact that our method and the Gold Standard algorithm produce comparable results shows the potential of our proposal, since with less information (only from a plane) and with a much simpler processing (solving a single second-order equation), we obtain the epipolar direction with similar accuracy.

Links:

4 Seminar 28-4-2005

Speaker: Juan Andrade-Cetto ()
Title: Unscented Transformation of Vehicle States in SLAM
Abstract:

In this article we propose an algorithm to reduce the effects caused by linearization in the typical EKF approach to SLAM. The technique consists in computing the vehicle prior using an Unscented Transformation. The UT allows a better nonlinear mean and variance estimation than the EKF. There is no need however in using the UT for the entire vehicle-map state, given the linearity in the map part of the model. By applying the UT only to the vehicle states we get more accurate covariance estimates. The a posteriori estimation is made using a fully observable EKF step, thus preserving the same computational complexity as the EKF with sequential innovation. Experiments over a standard SLAM data set show the behavior of the algorithm.

Links: ICRA-05-paper, ICRA-05-slides

3 Seminar 14-4-2005

Speaker: Josep M Porta ()
Title: On the Trilaterable Six-Degree-of-Freedom Parallel and Serial Manipulators
Abstract:

The inverse/direct kinematics of trilaterable serial/parallel manipulators can be stated as a system of distance constraints whose set of solutions can be determined using a sequence of trilaterations, possibly involving points at infinity. It is possible to decide whether a mechanism is trilaterable by relying only on its topology. Based on this fact, we here enumerate all trilaterable serial and in-parallel robots with six degrees of freedom. The relevance of the obtained family of manipulators is established when it is shown to contain the best-known commercial serial robots. As a result of this analysis, we come up with a general method to solve the inverse/direct kinematics of a wide family of manipulators.

Links: ICRA-05-paper, ICRA-05-slides

2 Seminar 31-3-2005 (Delayed to 7-4-2005)

Speaker: Francesc Moreno ()
Title: Optimal Illumination for Image and Video Relighting
Abstract:

It has been shown in the literature that image-based relighting of scenes with unknown geometry can be achieved through linear combinations of a set of pre-acquired reference images. Since the placement and brightness of the light sources can be controlled, it is natural to ask: what is the optimal way to illuminate the scene to reduce the number of reference images that are needed? We show that the best way to light the scene (i.e., the way that minimizes the number of reference images) is not using a sequence of single, compact light sources as is most commonly done, but rather to use a sequence of lighting patterns as given by an object-dependent lighting basis. While this lighting basis, which we call the optimal lighting basis (OLB), depends on camera and scene properties, we show that it can be determined as a simple calibration procedure before acquisition. We demonstrate through experiments on real and synthetic data that the optimal lighting basis significantly reduces the number of reference images that are needed to achieve a desired level of accuracy in the relit images. This reduction in the number of needed images is particularly critical in the problem of relighting in video, as corresponding points on moving objects must be aligned from frame to frame during each cycle of the lighting basis. We show, however, that the efficiencies gained by the optimal lighting basis makes relighting in video possible using only a simple optical flow alignment. We present several relighting results on real video sequences of moving objects, moving faces, and scenes containing both. In each case, although a single video clip was captured, we are able to relight again and again, controlling the lighting direction, extent, and color.

Links: Siggraph05-Sketch, Slides

1 Seminar 10-3-2005

Speaker: Alejandro Agostini ()
Title: Feasible Control of Complex Systems using Automatic Learning
Abstract:

Robotics applications often involve dealing with complex dynamic systems. In these cases coping with control requirements with conventional techniques is hard to achieve and a big effort has to be done in the design and tuning of the control system. An alternative to conventional control techniques is the use of automatic learning systems that could learn control policies automatically, by means of the experience. But the amount of experience required in complex problems is intractable unless some generalization is performed. Many learning techniques have been proposed to deal with this challenge but the applicability of them in a complex control task is still impracticable because their bad learning convergence or insufficient generalization. In this work a new learning technique, that explodes a kind of generalization called categorization, is used in a complex control task. The results obtained show that it is possible to learn, in short time and with good convergence, a control policy that outperforms a classical PID control tuned for the specific task of controlling a manipulator with high inertia and variable load.

Links: Slides