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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 (2019 - 2018) of the Perception and Manipulation

  • A. Jevtić, A. Flores, G. Alenyà, G. Chance, P. Caleb-Solly, S. Dogramadzi and C. Torras. Personalized robot assistant for support in dressing. IEEE Transactions on Cognitive and Developmental Systems, 2019, to appear.

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  • G. Canal, G. Alenyà and C. Torras. Adapting robot task planning to user preferences: An assistive shoe dressing example. Autonomous Robots, 2019, to appear.

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  • A. Agudo and F. Moreno-Noguer. Robust spatio-temporal clustering and reconstruction of multiple deformable bodies. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(4): 971-984, 2019.

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  • A. Agudo and F. Moreno-Noguer. Shape basis interpretation for monocular deformable 3D reconstruction. IEEE Transactions on Multimedia, 21(4): 821-834, 2019.

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  • F. Amadio, A. Colomé and C. Torras. Exploiting symmetries in reinforcement learning of bimanual robotic tasks. IEEE Robotics and Automation Letters, 4(2): 1838-1845, 2019.

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  • F. Simao, F. Martínez-Jerónimo, V. Blasco, F. Moreno-Noguer, J.M. Porta, J. Pestana, A. Soarez, D. Raldúa and C. Barata. Using a new high-throughput video-tracking platform to assess behavioural changes in Daphnia magna exposed to neuro-active drugs. Science of the Total Environment, 662: 160-167, 2019.

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  • M. Villamizar, A. Sanfeliu and F. Moreno-Noguer. Online learning and detection of faces with low human supervision. The Visual Computer, 35(3): 349-370, 2019.

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  • A. Suárez, C. Torras and G. Alenyà. Practical resolution methods for MDPs in robotics exemplified with disassembly planning. IEEE Robotics and Automation Letters, 4(3): 2282-2288, 2019.

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  • R. Jangir, G. Alenyà and C. Torras. Learning cloth manipulation with demonstrations. Technical Report IRI-TR-19-01, Institut de Robòtica i Informàtica Industrial, CSIC-UPC, 2019.

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  • G. Rotger, F. Moreno-Noguer, F. Lumbreras and A. Agudo. Detailed 3D face reconstruction from a single RGB image, 27th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, 2019, Plzen, Czech Republic, to appear.

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  • G. Rotger, F. Moreno-Noguer, F. Lumbreras and A. Agudo. Single view facial hair 3D reconstruction, 2019 Iberian Conference on Pattern Recognition and Image Analysis, 2019, Madrid, Spain, to appear.

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  • G. Canal, M. Cashmore, S. Krivić, G. Alenyà, D. Magazzeni and C. Torras. Probabilistic planning for robotics with ROSPlan, 2019 Towards Autonomous Robotic Systems Conference, 2019, London, Springer, to appear.

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  • I. Tkach, A. Jevtić, S.Y. Nof and Y. Edan. A modified distributed bees algorithm for multi-sensor task allocation. Sensors, 18(3): 759:1-16, 2018.

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  • A. Suárez, G. Alenyà and C. Torras. Interleaving hierarchical task planning and motion constraint testing for dual-arm manipulation, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018, Madrid, pp. 4061-4066.

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  • F. Ramos, C.O. Scrob, A.S. Vázquez, R. Fernández and A. Olivares-Alarcos. Skill-oriented designer of conceptual robotic structures, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018, Madrid, pp. 5679-5684.

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  • V. Vaquero, A. Sanfeliu and F. Moreno-Noguer. Deep lidar CNN to understand the dynamics of moving vehicles, 2018 IEEE International Conference on Robotics and Automation, 2018, Brisbane, Australia, pp. 4504-4509.

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  • V. Vaquero, A. Sanfeliu and F. Moreno-Noguer. Hallucinating dense optical flow from sparse lidar for autonomous vehicles, 24th International Conference on Pattern Recognition, 2018, Beijing, China, pp. 1959-1964.

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  • A. Agudo, M. Pijoan and F. Moreno-Noguer. Image collection pop-up: 3D reconstruction and clustering of rigid and non-rigid categories, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, Salt Lake City, pp. 2607-2615.

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  • J. Garcia, A. Agudo and F. Moreno-Noguer. Vehicle pose estimation using G-Net: Multi-class localization and depth estimation, 21st Catalan Conference on Artificial Intelligence, 2018, Roses, in Artificial Intelligence Research and Development, Vol 308 of Frontiers in Artificial Intelligence and Applications, pp. 355-364, 2018, IOS Press.

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  • A. Pumarola, A. Agudo, L. Porzi, A. Sanfeliu, V. Lepetit and F. Moreno-Noguer. Geometry-aware network for non-rigid shape prediction from a single view, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, Salt Lake City, pp. 4681-4690.

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  • C. Torras. Aprenentatge Automàtic en Xarxes i Robots: Reptes Tecnocientífics i Implicacions Ètiques. Institut d'Estudis Catalans. Secció de Ciències i Tecnologia, 2018.

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  • A. Pumarola, A. Agudo, A. Sanfeliu and F. Moreno-Noguer. Unsupervised person image synthesis in arbitrary poses, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, Salt Lake City, pp. 8620-8628.

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  • C. Torras. Social networks and robot companions: Technology, ethics and science fiction. Mètode Science Studies Journal, 99: 47-53, 2018.

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  • A. Taranović, A. Jevtić, J. Hernández-Farigola, N. Tantinyà, C. Abdelnour and C. Torras. Resource-based modality selection in robot-assisted cognitive training, 2018 ACM/IEEE International Conference on Human-Robot Interaction, 2018, Chicago, USA, pp. 255-256.

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  • C. Torras and R. López de Mántaras. Special issue editorial: Interconnectats: Màquines i humans davant del segle 10101. Mètode Science Studies Journal, 99: 2-3, 2018.

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  • S. Thiusius, B. Anders, S. Christian, N. Wantia, J. Rossmann, D. Martínez, G. Alenyà, C. Torras, U. Ales, N. Bojan, K. Aljaž, F. Wörgötter, E.E. Aksoy, J. Papon, S. Haller, J. Piater and N. Krüger. Teaching a robot the semantics of assembly tasks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(5): 670-692, 2018.

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  • A. Andriella, G. Alenyà, J. Hernández-Farigola and C. Torras. Deciding the different robot roles for patient cognitive training. International Journal of Human-Computer Studies, 117: 20-29, 2018.

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  • M. Vila, G. Canal and G. Alenyà. Towards safety in physically assistive robots: eating assistance, 2018 IROS Workshop on Robots for Assisted Living, 2018, Madrid, pp. 1-4.

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  • M. Villamizar, J. Andrade-Cetto, A. Sanfeliu and F. Moreno-Noguer. Boosted random ferns for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(2): 272-288, 2018.

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  • A. Taranović, A. Jevtić and C. Torras. Adaptable multimodal interaction framework for robot-assisted cognitive training, 2018 ACM/IEEE International Conference on Human-Robot Interaction, 2018, Chicago, USA, pp. 327-328.

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  • P. Jiménez. Software tools for the cognitive development of autonomous robots. In Autonomy in Future Military and Security Technologies: Implications for Law, Peace, and Conflict, 18-32. Richardson Institute - Lancaster University , 2018.

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  • A. Jevtić, A. Colomé, G. Alenyà and C. Torras. Robot motion adaptation through user intervention and reinforcement learning. Pattern Recognition Letters, 105: 67-75, 2018.

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  • G. Rotger, F. Lumbreras, F. Moreno-Noguer and A. Agudo. 2D-to-3D facial expression transfer, 24th International Conference on Pattern Recognition, 2018, Beijing, China, pp. 2008-2013.

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  • M. Pirashvili, L. Steinberg, F. Belchí, M. Niranjan, J.G. Frey and J. Brodzki. Improved understanding of aqueous solubility modeling through topological data analysis. Journal of Cheminformatics, 10(54): 1-14, 2018.

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  • A. Agudo and F. Moreno-Noguer. Force-based representation for non-rigid shape and elastic model estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(9): 2137-2150, 2018.

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  • A. Agudo and F. Moreno-Noguer. Deformable motion 3D reconstruction by union of regularized subspaces, 25th IEEE International Conference on Image Processing, 2018, Athens, Greece, pp. 2930-2934.

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  • A. Sabir, F. Moreno-Noguer and L. Padró. Visual re-ranking with natural language understanding for text spotting, 14th Asian Conference on Computer Vision, 2018, , to appear.

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  • E. Corona, G. Alenyà, A. Gabás and C. Torras. Active garment recognition and target grasping point detection using deep learning. Pattern Recognition, 74: 629-641, 2018.

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  • G. Canal, E. Pignat, G. Alenyà, S. Calinon and C. Torras. Joining high-level symbolic planning with low-level motion primitives in adaptive HRI: application to dressing assistance, 2018 IEEE International Conference on Robotics and Automation, 2018, Brisbane, Australia, pp. 3273-3278.

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  • A. Sabir, F. Moreno-Noguer and L. Padró. Enhancing text spotting with a language model and visual context information, 21st Catalan Conference on Artificial Intelligence, 2018, Roses, in Artificial Intelligence Research and Development, Vol 308 of Frontiers in Artificial Intelligence and Applications, pp. 271-280, 2018, IOS Press.

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  • A. Agudo and F. Moreno-Noguer. A scalable, efficient, and accurate solution to non-rigid structure from motion. Computer Vision and Image Understanding, 167: 121-133, 2018.

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  • C. Torras. The Vestigial Heart. A Novel of the Robot Age. The MIT Press, 2018.

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  • A. Sabir, F. Moreno-Noguer and L. Padró. Visual semantic re-ranker for text spotting, 23rd Iberoamerican Congress on Pattern Recognition, 2018, Madrid, in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Vol 11401 of Lecture Notes in Computer Science, pp. 884-892, 2019.

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  • A. Colomé and C. Torras. Dimensionality reduction for dynamic movement primitives and application to bimanual manipulation of clothes. IEEE Transactions on Robotics, 34(3): 602-615, 2018.

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  • A. Colomé and C. Torras. Dimensionality reduction in learning Gaussian mixture models of movement primitives for contextualized action selection and adaptation. IEEE Robotics and Automation Letters, 3(4): 3922-3929, 2018.

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  • J. Borràs Sol, R. Heudorfer, S. Rader, P. Kaiser and T. Asfour. The KIT swiss knife gripper for disassembly tasks: a multi-functional gripper for bimanual manipulation with a single arm, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018, Madrid, pp. 4590-4597.

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  • M. Alberich-Carramiñana, J. Àlvarez and F. Dachs. Constancy regions of mixed multiplier ideals in two-dimensional local rings with rational singularities. Mathematische Nachrichten, 291(2): 245-263, 2018.

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  • G. Chance, A. Jevtić, P. Caleb-Solly, G. Alenyà, C. Torras and S. Dogramadzi. "Elbows out" - predictive tracking of partially occluded pose for robot-assisted dressing. IEEE Robotics and Automation Letters, 3(4): 3598-3605, 2018.

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  • P. Jiménez and G. Čubrić. Robots in the textile and fashion industries: facts and prospectives, 2018 Scientific-Professional Symposium Textile Science and Economy, 2018, Zagreb, pp. 124-131.

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  • A. Ramisa, F. Yan, F. Moreno-Noguer and K. Mikolajczyk. BreakingNews: Article annotation by image and text processing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(5): 1072-1085, 2018.

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  • A. Pumarola, A. Agudo, A.M. Martinez, A. Sanfeliu and F. Moreno-Noguer. GANimation: Anatomically-aware facial animation from a single image, 15th European Conference on Computer Vision, 2018, Munich, in Computer Vision - ECCV 2018, Vol 11214 of Lecture Notes in Computer Science, pp. 835-851, 2018.

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  • M. Alberich-Carramiñana, J. Àlvarez, F. Dachs and V. González-Alonso. Poincaré series for mixed multiplier ideals, XVI Encuentros de Álgebra Computacional y Aplicaciones, 2018, Zaragoza, in 43, pp. 27-30, Monografías de la Real Academia de Ciencias Exactas, Físicas, Químicas y Naturales de Zaragoza.

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  • S. Foix, G. Alenyà and C. Torras. Task-driven active sensing framework applied to leaf probing. Computers and Electronics in Agriculture, 147: 166-175, 2018.

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  • A. Taranović, A. Jevtić and C. Torras. Adaptive modality selection algorithm in robot-assisted cognitive training, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018, Madrid, pp. 4456-4461.

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  • M. Alberich-Carramiñana, J. Àlvarez, F. Dachs and V. González-Alonso. Multiplicity and Poincaré series for mixed multiplier ideals, 5th Joint Meeting of the Czech, Slovenian, Austrian, Slovak and Catalan Mathematical Societies, 2018, Bratislava, Slovakia, pp. 59-61.

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Perception and Manipulation Laboratory

The Perception and Manipulation Laboratory is equipped with 2 workcells: one with 2 standard manipulators and an XY positioner, and the other with 2 WAM arms in a reconfigurable arrangement. Additionally, researchers can find a 3 fingered hand, a Delta haptic interface, force sensors, several conventional cameras, and high speed, high resolution, and 3D cameras. Laboratory service offers quick experimental setup, several standardized software tools, and expertise in robot control and perception algorithms. It also hosts the Humanoid Lab initiative, with 15 small humanoid robots.

Perception and Manipulation Laboratory
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