General information on the IRI research seminars


Forthcoming seminars


Seminar 26-09-2018

Speaker: Andres F. Marmol ()
Title: ArthroSLAM: Robust camera localization and dense 3D reconstruction for minimally invasive orthopaedic surgery
Abstract:

Minimally invasive orthopaedic surgeries are very challenging procedures that require the manipulation of instruments in limited intraarticular space using distorted and sometimes uninformative images. Localizing the arthroscope reliably and at all times w.r.t. surrounding tissue is of fundamental importance to prevent unintended injury to patients. A detailed reconstruction of the surrounding tissues is also of great interest for diagnostic purposes. In this talk I will present our research on a visual Simultaneous Localisation and Mapping (SLAM) system, termed ArthroSLAM, for robotic-assisted minimally invasive orthopaedics. The talk will address three fundamental aspects of the system: Feature detection and matching, robust camera localization and accurate dense 3D reconstruction. ArthroSLAM fuses the information obtained from the arthroscope, an external camera mounted on an arthroscope holder, and the odometry of a robotic arm manipulating the scope. The resulting robust localization prior is later used in a keyframe-based MultiView stereo for dense 3D reconstruction. To the best of our knowledge, this is the first demonstration of a SLAM system in arthroscopy. We compare our method against various strategies, including state-of-the-art SLAM systems, in a number of experiments using realistic setups with human cadaver knee joints. Our system is shown to outperform alternative strategies under various challenging conditions, both in terms of localization and reconstruction accuracy. Additional experiments conducted with synthetically degraded data also validate the robustness of our system.

Links: Speaker info

Seminar 27-9-2018

Speaker: Ricard Bordalba ()
Title: A Singularity-Robust LQR Controller for Parallel Robots
Abstract:

Parallel robots exhibit the so-called forward singularities, which complicate substantially the planning and control of their motions. Often, the issues introduced by such configurations are circumvented by restricting the motions to singularity- free regions of the workspace. However, this comes at the expense of reducing the motion range of the robot substantially. It is for this reason that, recently, efforts are underway to control singularity-crossing trajectories. This paper proposes a reliable controller to stabilize such kind of trajectories. The controller is based on the classical theory of linear quadratic regulators, which we adapt appropriately to the case of parallel robots. As opposed to traditional computed-torque methods, the obtained controller does not rely on expensive inverse dynamics computations. Instead, it uses an optimal control law that is easy to evaluate, and does not generate instabilities at forward singularities. The performance of the controller is exemplified on a five-bar parallel robot accomplishing two tasks that require the traversal of singularities.

Links: Speaker info, IROS-18


Speaker: Adrià Colomé ()
Title: Dimensionality Reduction in Learning Gaussian Mixture Models of Movement Primitives for Contextualized Robot Action Selection
Abstract:

Robotic manipulation often requires adaptation to changing environments. Such changes can be represented by a certain number of contextual variables that may be observed or sensed in different manners. When learning and representing robot motion –usually with movement primitives–, it is desirable to adapt the learned behaviors to the current context. Moreover, different actions or motions can be considered in the same framework, using contextualization to decide which action applies to which situation. Such frameworks, however, may easily become large-dimensional, thus requiring to reduce the dimensionality of the parameters space, as well as the amount of data needed to generate and improve the model over experience. In this paper, we propose an approach to obtain a generative model from a set of actions that share a common feature. Such feature, namely a contextual variable, is plugged into the model to generate motion. We encode the data with a Gaussian Mixture Model in the parameter space of Probabilistic Movement Primitives (ProMPs), after performing Dimensionality Reduction (DR) on such parameter space. We append the contextual variable to the parameter space and obtain the number of Gaussian components, i.e., different actions in a dataset, through Persistent Homology . Then, using multimodal Gaussian Mixture Regression (GMR), we can retrieve the most likely actions given a contextual situation and execute them. After actions are executed, we use Reward-Weighted Responsibility GMM (RWR-GMM) update the model after each execution. Experimentation in 3 scenarios shows that the method drastically reduces the dimensionality of the parameter space, thus implementing both action selection and adaptation to a changing situation in an efficient way.

Links: Speaker info, IROS-18


Speaker: Júlia Borràs ()
Title: The KIT Swiss Knife Gripper for Disassembly Tasks: A Multi-Functional Gripper for Bimanual Manipulation with a Single Arm
Abstract:

This work presents the concept of a robotic gripper designed for the disassembly of electromechanical devices that comprises several innovative ideas. Novel concepts include the ability to interchange built-in tools without the need to grasp them, the ability to reposition grasped objects in-hand, the capability of performing classic dual arm manipulation within the gripper and the utilization of classic industrial robotic arms kinematics within a robotic gripper. We analyze state of the art grippers and robotic hands designed for dexterous in-hand manipulation and extract common characteristics and weak points. The presented concept is obtained from the task requirements for disassembly of electromechanical devices and it is then evaluated for general purpose grasping, in-hand manipulation, and operations with tools. We further present the CAD design for a first prototype.

Links: Speaker info, IROS-18


Speaker: Alejandro Suárez ()
Title: Interleaving hierarchical task planning and motion constraint testing for dual-arm manipulation
Abstract:

In recent years the topic of combining motion and symbolic planning to perform complex tasks in the field of robotics has received a lot of attention. The underlying idea is to have access at once to the reasoning capabilities of a task planner and to the ability of the motion planner to verify that the plan is feasible from a physical and geometrical point of view. The present work describes a framework to perform manipulation tasks that require the use of two robotic manipulators. To do so we employ a Hierarchical Task Network (HTN) planner interleaved with geometric constraint verification. In this framework we also consider observation actions and handle noisy perceptions from a probabilistic perspective. These ideas are put into practice by means of an experimental set-up in which two Barrett WAM robots have to cooperatively solve a geometric puzzle. Our findings provide further evidence that considering explicitly physical constraints during task planning, rather than deferring their validation to the moment of execution, is advantageous in terms of execution time and breadth of situations that can be handled.

Links: Speaker info, IROS-18

Seminar 10-10-2018

Speaker: José del R. Millán ()
Title:
Abstract:

Links: Speaker info

Seminar 11-10-2018

Speaker: Teresa Vidal-Calleja ()
Title:
Abstract:

Links: Speaker info

Seminar 18-10-2018

Speaker: Dan Halperin ()
Title: Multi-Robot Motion Planning: The Easy, the Hard and the Uncharted
Abstract:

Early results in robot motion planning had forecast a bleak future for the field by showing that problems with many degrees of freedom, and in particular those involving fleets of robots, are intractable. Then came sampling-based planners, which have been successfully, and often easily, solving a large variety of problems with many degrees of freedom.
We strive to formally determine what makes a motion-planning problem with many degrees of freedom easy or hard. In the first part of the talk I'll describe our quest to resolve this (still wide open) problem, and some progress we have made in the context of multi-robot motion planning.
In the second part of the talk I'll review recent algorithms that we have developed for multi-robot motion planning, which come with near- or asymptotic-optimality guarantees.

Links: Speaker info