Research line

Perception and Manipulation Image

The research of PERCEPTION AND MANIPULATION group focuses on enhancing the perception, learning, and planning capabilities of robots to achieve higher degrees of autonomy and user-friendliness during everyday manipulation tasks. Some topics addressed are the geometric interpretation of perceptual information, construction of 3D object models, action selection and planning, reinforcement learning, and teaching by demonstration.

Head of line: Carme Torras Genís

Head of line

Tech. transfer

Our activity finds applications in several fields through collaboration with our technological partners

Research projects

We carry out projects from national and international research programmes.
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Learning by demonstration

We devise methods to learn object-action relations to accomplish tasks at different levels of abstraction, where object models are generated from visual and depth information, and actions, involving manipulation skills, are learned from demonstrations provided by a human using multimodal algorithms that combine vision and haptics.

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Research area 1 of Perception and Manipulation

Planning for perception and manipulation

We are interested in view planning for object modeling, as well as manipulation planning, with special interest in deformable objects. High-level task formulations are integrated with low-level geometry-based methods and simplified physical models, together with an on-line sensory-based treatment of uncertainty, so as to come up with specific sequences of motion commands.

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Research area 2 of Perception and Manipulation

Perception of rigid and non-rigid objects

Our objective is to investigate computer vision algorithms for interpreting and understanding scenes from images, with applications in robotics and medical imaging. In particular, our activities are concentrated on retrieving rigid and non-rigid shape, motion and camera pose from single images and video sequences.

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Research area 3 of Perception and Manipulation

These are the latest research projects of the Perception and Manipulation research line:

These are the most recent publications (2022 - 2021) of the Perception and Manipulation

  • F. Coltraro, J. Amorós, M. Alberich-Carramiñana and C. Torras. An inextensible model for the robotic manipulation of textiles. Applied Mathematical Modelling, 101: 832-858, 2022.

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  • S. Forgas, R. Huertas, A. Andriella and G. Alenyà. The effects of gender and personality of robot assistants on customers’ acceptance of their service. Service Bussiness, 16: 359–389, 2022.

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  • A. Agudo, V. Lepetit and F. Moreno-Noguer. Simultaneous completion and spatiotemporal grouping of corrupted motion tracks. The Visual Computer, 2022, to appear.

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  • G. Canal, C. Torras and G. Alenyà. Generating predicate suggestions based on the space of plans: an example of planning with preferences. User Modeling and User-Adapted Interaction, 2022, to appear.

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  • S. Forgas, R. Huertas, A. Andriella and G. Alenyà. How do consumers' gender and rational thinking affect the acceptance of entertainment social robots?. International Journal of Social Robotics, 14: 973–994, 2022.

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  • I. Garcia-Camacho, J. Borràs and G. Alenyà. Knowledge representation to enable high-level planning in cloth manipulation tasks, 2022 ICAPS Workshop on Knowledge Engineering for Planning and Scheduling, 2022, Singapore (Virtual), pp. 9.

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  • X. Xu, H. Chen, F. Moreno-Noguer, L. Jeni and F. De la Torre. 3D human pose, shape and texture from low-resolution images and videos. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.

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  • I. Garcia-Camacho, J. Borràs, B. Calli, A. Norton and G. Alenyà. Cloth manipulation and perception competition, 2022 ICRA Workshop on Representing and Manipulating Deformable Objects, 2022, Philadelphia, pp. 4.

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  • A. Pérez and A. Agudo. Matching and recovering 3D people from multiple views, 2022 IEEE Winter Conference on Applications of Computer Vision, 2022, Waikoloa, Hawaii, USA, pp. 1184-1193, IEEE.

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  • M. Alberich-Carramiñana, J. Amorós, F. Coltraro, C. Torras and M. Verdaguer. Morse cell decomposition and parametrization of surfaces from point clouds, 2022 Encuentros de Álgebra Computacional y Aplicaciones, 2022, Castellón, Spain, to appear.

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  • A. Agudo. Safari from visual signals: Recovering volumetric 3D shapes, 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, 2022, Singapore, pp. 2495-2499.

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  • F. Coltraro, J. Amorós, M. Alberich-Carramiñana and C. Torras. An inextensible model for the robotic manipulation of textiles, 2022 Congreso de ecuaciones diferenciales y aplicaciones XXVII, 2022, Zaragoza, Spain, pp. 87-87.

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  • P. Jiménez. Learning in autonomous and intelligent systems: Overview and biases from data sources. Arbor - Ciencia, Pensamiento y Cultura, 197(802): a627, 2022.

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  • J. Pareto, B. Román and C. Torras. Ethics for social robotics: A critical analysis, 2022 The Road to a Successful HRI: AI, Trust and ethicS Workshop, in the ACM/IEEE International Conference on Human-Robot Interaction (HRI 2022), 2022, Sapporo, Japan, pp. 1-3.

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  • W. Zhang, M. Dimiccoli and B.Y. Lim. Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning , 2022 ACM Conference on Human Factors in Computing Systems, 2022, New Orleans, pp. 1-32, ACM.

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  • C. Torras and L. Delicado. Blanco y frío como el Polo Norte y el Polo Sur: Seres humanos y robots. Sociology and Technoscience, 12(1): 257-275, 2022.

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  • A. Olivares-Alarcos, S. Foix, S. Borgo and G. Alenyà. OCRA – An ontology for collaborative robotics and adaptation. Computers in Industry, 132: 103627, 2022.

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  • E. Caldarelli, A. Colomé and C. Torras. Perturbation-Based stiffness inference in variable impedance control. IEEE Robotics and Automation Letters, 7(4): 8823-8830, 2022.

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  • I. Garcia-Camacho, J. Borràs, B. Calli, A. Norton and G. Alenyà. Household cloth object set: Fostering benchmarking in deformable object manipulation. IEEE Robotics and Automation Letters, 7(3): 5866-5873, 2022, to appear.

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  • A. Andriella, C. Torras, C. Abdelnour and G. Alenyà. Introducing CARESSER: A framework for in situ learning robot social assistance from expert knowledge and demonstrations. User Modeling and User-Adapted Interaction, 2022.

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  • S. Izquierdo, G. Canal, C. Rizzo and G. Alenyà. Improved task planning through failure anticipation in human-robot collaboration, 2022 IEEE International Conference on Robotics and Automation, 2022, Philadelphia, Pennsylvania, USA, pp. 7875-7880.

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  • A. San-Miguel, G. Alenyà and V. Puig. Automated off-line generation of stable variable impedance controllers according to performance specifications. IEEE Robotics and Automation Letters, 7(3): 5874-5881, 2022, to appear.

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  • J. Borràs. Effective grasping enables successful robot-assisted dressing. Science robotics, 7(65): eabo7229, 2022.

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  • D.F. Ordoñez, A. Agudo, F. Moreno-Noguer and M. Martin. An adaptable approach to learn realistic legged locomotion without examples, 2022 IEEE International Conference on Robotics and Automation, 2022, Philadelphia, Pennsylvania, USA, pp. 4671-4678.

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  • A. Agudo. Unsupervised 3D reconstruction and grouping of rigid and non-rigid categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(1): 519-532, 2022.

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  • C. Xu, A. Colomé, L. Sentis and C. Torras. Mixtures of controlled Gaussian processes for dynamical modeling of deformable objects, 2022 Learning for Dynamics & Control Conference, 2022, Stanford, CA, USA, pp. 415-426.

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  • A. Colomé and C. Torras. A topological extension of movement primitives for curvature modulation and sampling of robot motion. Autonomous Robots, 45: 725-735, 2021.

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  • I. Garcia-Camacho, J. Borràs and G. Alenyà. Benchmarking cloth manipulation using action graphs: an example in placing flat, 1st IROS Workshop on Benchmarking of robotic grasping and manipulation: protocols, metrics and data analysis, 2021, Prague, Czech Republic (Virtual), pp. 1-3.

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  • A. Olivares-Alarcos, S. Foix and G. Alenyà. Knowledge Representation for Explainability in Collaborative Robotics and Adaptation, 3rd International Workshop on Data meets Applied Ontologies, 2021, Bratislava, Slovakia (Virtual), pp. 1-8.

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  • F. Strazzeri and C. Torras. Topological representation of cloth state for robot manipulation. Autonomous Robots, 45: 737–754, 2021.

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  • G. Canal, C. Torras and G. Alenyà. Are preferences useful for better assistance?: A physically assistive robotics user study. ACM Transactions on Human-Robot Interaction, 10(4): 1-19, 2021.

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  • B.M. Lopes, J. Sousa and G. Alenyà. Machine learning methods for quality prediction in thermoplastics injection molding, 2021 IEEE International Conference on Electrical, Computer and Energy Technologies, 2021, Cape Town, South Africa (Virtual), pp. 1-6.

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  • V. Vaquero, I. del Pino, F. Moreno-Noguer, J. Solà, A. Sanfeliu and J. Andrade-Cetto. Dual-branch CNNs for vehicle detection and tracking on LiDAR data. IEEE Transactions on Intelligent Transportation Systems, 22(11): 6942-6953, 2021.

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  • B. Nicolás, X. Wu, M. Dimiccoli, J. Sierpowska, C. Saiz-Masvidal, C. Soriano-Mas, P. Radeva and L. Fuentemilla. Behavioural and neurophysiological signatures in the retrieval of individual memories of recent and remote real-life routine episodic events. Cortex, 141: 128-143, 2021.

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  • A. San-Miguel, V. Puig and G. Alenyà. Disturbance observer-based LPV feedback control of a N-DoF robotic manipulator including compliance through gain shifting. Control Engineering Practice, 115(104887), 2021.

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  • M. Alberich-Carramiñana, A. Fernández-Boix, J. Fernández, J. Guàrdia, E. Nart and J. Roé. Of limit key polynomials. Illinois Journal of Mathematics, 65(1): 201-229, 2021.

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  • J. Garcia, A. Agudo and F. Moreno-Noguer. E-DNAS: Differentiable neural architecture search for embedded systems, 25th International Conference on Pattern Recognition, 2021, Milan, Italy (Virtual), pp. 4704-4711.

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  • M.A. Roa, M.R. Dogar, J. Pagès, C. Vivas, A. Morales, N. Correll, M. Görner, J. Rosell, S. Foix, R. Memmesheier and F. Ferro. Mobile manipulation hackathon: moving into real world applications. IEEE Robotics and Automation Magazine, 28(2): 112-124, 2021.

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  • A. Carfi, T. Patten, Y. Kuang, A. Hammoud, M. Alameh, E. Maiettini, A.I. Weinberg, D. Faria, F. Mastrogiovanni, G. Alenyà, L. Natale, V. Perdereau, M. Vincze and A. Billard. Hand-Object Interaction: From Human Demonstrations to Robot Manipulation. Frontiers in Robotics and AI, 8: 714023, 2021.

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  • P. Goncalves, A. Olivares-Alarcos, J. Bermejo-Alonso, S. Borgo, M. Diab, M.K. Habib, H. Nakawala, V.R. Kumar, R. Sanz, E. Tosello and H. Li. IEEE Standard for autonomous robotics ontology [Standards]. IEEE Robotics and Automation Magazine, 28(3): 171-173, 2021.

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  • A. Agudo. Total estimation from RGB video: On-line camera self-calibration, non-rigid shape and motion, 25th International Conference on Pattern Recognition, 2021, Milan, Italy (Virtual), pp. 8140-8147.

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  • N. Hansen, R. Jangir, Y.S. , G. Alenyà, P. Abbeel, A.A. , L.P. and X.W.. Self-supervised policy adaptation during deployment, 9th International Conference on Learning Representations, 2021, (Virtual).

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  • A. Suárez, A. Andriella, A. Taranović, J. Segovia, C. Torras and G. Alenyà. Automatic learning of cognitive exercises for socially assistive robotics, 30th IEEE International Symposium on Robot and Human Interactive Communication, 2021, Vancouver, Canada, pp. 139-146.

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  • A. Rodríguez, J. Lobo and J.M. Font. Systematic review on wearable lower-limb exoskeletons for gait training in neuromuscular impairments. Journal of NeuroEngineering and Rehabilitation, 18(22), 2021.

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  • A. Cartas, P. Radeva and M. Dimiccoli. Modeling long-term interactions to enhance action recognition, 25th International Conference on Pattern Recognition, 2021, Milan, Italy (Virtual), pp. 10351-10358.

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  • A. Agudo. Piecewise Bézier space: Recovering 3D dynamic motion from video, 28th IEEE International Conference on Image Processing, 2021, Anchorage, AK, USA, pp. 3268-3272.

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  • A. Vakhitov, L. Ferraz, A. Agudo and F. Moreno-Noguer. Uncertainty-Aware Camera Pose Estimation from Points and Lines, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, Nashville, TN, USA (Virtual), pp. 4657-4666, Computer Vision Foundation.

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  • A. Chacón, P. Ponsa and C. Angulo. Usability study through a human-robot collaborative workspace experience. Designs, 5(2): 35, 2021.

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  • A. Andriella, H. Siqueira, D. Fu, S. Magg, P. Barros, S. Wermter, C. Torras and G. Alenyà. Do I have a personality? Endowing care robots with context-dependent personality traits. International Journal of Social Robotics, 13(8): 2081-2102, 2021.

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  • S. Felicioni and M. Dimiccoli. Interaction-GCN: a Graph Convolutional Network based framework for social interaction recognition in egocentric videos, 28th IEEE International Conference on Image Processing, 2021, Anchorage, AK, USA, pp. 2348-2352.

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  • M. Arduengo, C. Torras and L. Sentis. Robust and adaptive door operation with a mobile robot. Intelligent Service Robotics, 14(3): 409-425, 2021.

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  • P. Noriega, T. Ausín, H. Mueller, M. Ellman, A.R. Cardoso, F. Aguiar, D. López, M.A. Toboso, A. Wagner, A. García, S. Degli Esposti, L.M. Miller, P. Jiménez, J. Pareto and C. Torras. Challenge 6: Ethical, legal, economic, and social implications. In Artificial Intelligence, Robotics & Data Science, 121-141. Consejo Superior de Investigaciones Científicas, 2021.

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  • A. Cartas, E. Talavera, P. Radeva and M. Dimiccoli. Understanding event boundaries for egocentric activity recognition from photo-streams, 2021 ICPR International Workshops and Challenges, 2021, Milan, Italy (Virtual), in Pattern Recognition. ICPR International Workshops and Challenges. Proceedings, Part III, Vol 12663 of Lecture Notes in Computer Science, pp. 334-347, 2021, Springer International Publishing.

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  • A. Chacón, P. Ponsa and C. Angulo. Cognitive interaction analysis in human–robot collaboration using an assembly task. Electronics, 10(11): 1317, 2021.

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  • C. Torras. Ciència i ficció: quina inspira quina?. In ENTRE LA MANO Y LA CABEZA: Trece puntos de encuentro entre la ciencia y la literatura, 117-125. Inspiraciencia, CSIC Catalunya, 2021.

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  • F. Manyà, A. Colomé, G. García, D. Ríos, A. Torres, E. Armengol, C. Blum, T. Flaminio, L. Godo, J. Levy, P. Meseguer and J. Segovia. Challenge 1: Integrating knowledge, reasoning and learning. In Artificial Intelligence, Robotics & Data Science, 19-37. Consejo Superior de Investigaciones Científicas, 2021.

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  • A. Ruiz and J. Verbeek. Anytime inference with distilled hierarchical neural ensembles, 35th AAAI Conference on Artificial Intelligence, 2021, (Virtual), pp. 9463-9471.

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  • A. Duarte, S. Palaskar, L. Ventura, D. Ghadiyaram, K. DeHaan, F. Metze, J. Torres and X. Giro-i-Nieto. How2Sign: A large-scale multimodal dataset for continuous American sign language, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, Nashville, TN, USA (Virtual), pp. 2734-2743, Computer Vision Foundation.

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  • J. Sanchez, A. Pumarola and F. Moreno-Noguer. PhysXNet: A customizable approach for learning cloth dynamics on dressed people, 2021 International Conference on 3D Vision, 2021, London, UK (Virtual), pp. 879-888.

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  • N. Osman, D. López, V. Gallego, D. Ríos, M. San Miguel, M.A. Toboso, P. Noriega, J.A. Rodríguez, J. Sabater, C.S. , D. Zurro, S. Degli Esposti and G. Alenyà. Challenge 2: Multiagent systems. In Artificial Intelligence, Robotics & Data Science, 39-52. Consejo Superior de Investigaciones Científicas, 2021.

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  • A. Ruiz, A. Agudo and F. Moreno-Noguer. Generating attribution maps with disentangled masked backpropagation, 2021 International Conference on Computer Vision, 2021, (Virtual), pp. 885-894.

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  • M. Arduengo, A. Arduengo, A. Colomé, J. Lobo and C. Torras. Human to robot whole-body motion transfer, 2020 IEEE-RAS International Conference on Humanoid Robots, 2021, Munich, Germany (Virtual), pp. 299-305.

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  • E. Ramon, G. Triginer, J. Escur, A. Pumarola, J. García, X. Giro-i-Nieto and F. Moreno-Noguer. H3D-Net: Few-shot high-fidelity 3D head reconstruction, 2021 International Conference on Computer Vision, 2021, (Virtual), pp. 5620-5629, to appear.

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  • G. Alenyà, J. Villagrá, R.E. Fernández, P. González, R.E. Haber, A.R. Jiménez, A. Ribeiro, E. Rocón, J. Borràs, F. Moreno-Noguer and C. Torras. Challenge 4: Intelligent robotics. In Artificial Intelligence, Robotics & Data Science, 81-99. Consejo Superior de Investigaciones Científicas, 2021.

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  • A. Suárez, J. Segovia, C. Torras and G. Alenyà. Online action recognition, 35th AAAI Conference on Artificial Intelligence, 2021, (Virtual), pp. 11981-11989.

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  • M. Dimiccoli, L. Garrido, G. Rodríguez and H. Wendt. Graph constrained data representation learning for human motion segmentation, 2021 International Conference on Computer Vision, 2021, (Virtual), pp. 1440-1449.

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  • A. Hernandez Ruiz, A. Vilalta and F. Moreno-Noguer. Neural Cellular Automata manifold, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, Nashville, TN, USA (Virtual), pp. 10015-10023, Computer Vision Foundation.

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  • Q. Guan, Z. Qu, M. Zeng, J. Shen and J. Du. CGP Box: An effective direction representation strategy for oriented object detection in remote sensing images. International Journal of Remote Sensing, 42(17): 6666-6687, 2021.

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  • J. Pareto. Prolegómenos a una ética para la robótica social. DILEMATA, Revista Internacional de Éticas Aplicadas(34): 71-87, 2021.

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  • B.M. Lopes, J. Sousa and G. Alenyà. Data acquisition and monitoring system for legacy injection machines, 9th IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, 2021, Hong Kong, China (Virtual).

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  • N. Ugrinovic, A. Ruiz, A. Agudo, A. Sanfeliu and F. Moreno-Noguer. Body size and depth disambiguation in multi-person reconstruction from single images, 2021 International Conference on 3D Vision, 2021, London, UK (Virtual), pp. 53-63.

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  • M. Alberich-Carramiñana, A. Ferragut and J. Llibre. Quadratic planar differential systems with algebraic limit cycles via quadratic plane Cremona maps. Advances in Mathematics , 389: 107924, 2021.

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  • W. Guo, E. Corona, F. Moreno-Noguer and X. Alameda. PI-Net: Pose interacting network for multi-person monocular 3D pose estimation, 21st IEEE Winter Conference on Applications of Computer Vision, 2021, (Virtual), pp. 2795-2805.

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  • M. Dimiccoli, H. Wendt and P. Batlle. Learning grounded word meaning representations on similarity graphs, 16th Conference on Empirical Methods in Natural Language Processing, 2021, Punta Cana, Dominican Republic, pp. 4760-4769.

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  • A. Pumarola, E. Corona, G. Pons-Moll and F. Moreno-Noguer. D-NeRF: Neural radiance fields for dynamic scenes, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, Nashville, TN, USA (Virtual), pp. 10313-10322, Computer Vision Foundation.

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  • M. Alberich-Carramiñana, P. Almirón, G. Blanco and A. Melle-Hernández. The minimal Tjurina number of irreducible germs of plane curve singularities. Indiana University Mathematics Journal, 70(4): 1211-1220, 2021.

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  • J. Shen, A. Ruiz, A. Agudo and F. Moreno-Noguer. Stochastic Neural Radiance Fields: Quantifying uncertainty in implicit 3D representations, 2021 International Conference on 3D Vision, 2021, London, UK (Virtual), pp. 972-981.

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  • M. Dalmasso, A. Garrell Zulueta, J.E. Domínguez, P. Jiménez and A. Sanfeliu. Human-robot collaborative multi-agent path planning using Monte Carlo tree search and Social Reward Sources, 2021 IEEE International Conference on Robotics and Automation, 2021, Xian, China, pp. 10133-10138.

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  • E. Corona, A. Pumarola, G. Alenyà, G. Pons-Moll and F. Moreno-Noguer. SMPLicit: Topology-aware generative model for clothed people, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, Nashville, TN, USA (Virtual), pp. 11870-11880, Computer Vision Foundation.

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  • M. Alberich-Carramiñana, G. Blanco, I. Gálvez Carrillo, M. Garrote-López, E. Miranda (editors). Extended Abstracts GEOMVAP 2019. Volume 15 of Trends in Mathematics-Research Perspectives CRM Barcelona. Springer-Birkhäuser, 2021.

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  • G. Simarro, D. Calvete, T. Plomaritis, F. Moreno-Noguer, I. Giannoukakou, J. Montes and R. Duran. The influence of camera calibration on nearshore bathymetry estimation from UAV videos. Remote Sensing, 13(1): 150, 2021.

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  • J. Laplaza, A. Pumarola, F. Moreno-Noguer and A. Sanfeliu. Attention deep learning based model for predicting the 3D human body pose using the robot human handover phases, 30th IEEE International Symposium on Robot and Human Interactive Communication, 2021, Vancouver, Canada, pp. 161-166.

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  • A. Chatziagapi, S. Athar, F. Moreno-Noguer and D. Samaras. SIDER: Single-image neural optimization for facial geometric detail recovery, 2021 International Conference on 3D Vision, 2021, London, UK (Virtual), pp. 815-824.

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  • B.W. Smith, J. Lobo, D.K. Zondervan, C. Lew, V. Chan, C. Chou, S. Toledo, D.J. Reinkensmeyer, S. Shaw and S.C. Cramer. Using a bimanual lever-driven wheelchair for arm movement practice early after stroke: A pilot, randomized, controlled, single-blind trial. Clinical Rehabilitation, 35(11): 1577-1589, 2021.

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  • M. Arduengo, A. Colomé, J. Borràs, L. Sentis and C. Torras. Task-adaptive robot learning from demonstration with Gaussian process models under replication. IEEE Robotics and Automation Letters, 6(2): 966-973, 2021.

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  • J. Pareto, B. Román and C. Torras. The ethical issues of social assistive robotics: A critical literature review. Technology in Society, 67: 101726, 2021.

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  • J. Lundell, E. Corona, T. Nguyen Le, F. Verdoja, P. Weinzaepfel, G. Rogez, F. Moreno-Noguer and V. Kyrki. Multi-FinGAN: Generative coarse-to-fine sampling of multi-finger grasps, 2021 IEEE International Conference on Robotics and Automation, 2021, Xian, China, pp. 4495-4501.

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Perception and Manipulation Laboratory

The Perception and Manipulation Laboratory is the main facility devoted to experimentation on the topics of the research line. It occupies 142 m² in the second floor of the Faculty, and a large part hosts a life-scale mock-up of a (quite) small apartment (35 m²). Two PAL one-armed mobile TIAGo robots dwell within the apartment, but are occasionally taken out for experimentation elsewhere. There is also a manipulation area equipped with two WAM robot arms and a KINOVA manipulator, and workplaces are distributed along the perimeter. Next to the laboratory, with a direct view through a window, there is the Scientific and Technical Lab support office.

The Laboratory is further equipped with commercial and self-developed grippers, sensing devices, and augmented reality appliances. Laboratory service offers quick experimental setup, several standardized software tools, and expertise in robot control and perception algorithms. It also hosted the Humanoids Lab Initiative in the past, and we still keep 15 small humanoid robots for educational or promotional purposes.

Perception and Manipulation Laboratory
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