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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 (2018 - 2017) of the Perception and Manipulation

  • 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 Computer Society Conference on Computer Vision and Pattern Recognition, 2018, Salt Lake City, to appear.

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  • A. Pumarola, A. Agudo, A. Sanfeliu and F. Moreno-Noguer. Unsupervised person image synthesis in arbitrary poses, 2018 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2018, Salt Lake City, to appear.

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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, 2018, to appear.

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

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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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  • 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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  • 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, 2018, 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, 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. Rotger, F. Lumbreras, F. Moreno-Noguer and A. Agudo. 2D-to-3D facial expression transfer, 2018 International Conference on Pattern Recognition, 2018, Beijing, China, to appear.

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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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  • A. Agudo and F. Moreno-Noguer. Robust spatio-temporal clustering and reconstruction of multiple deformable bodies. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, to appear.

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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, 2018, to appear.

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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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  • 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, 2018, to appear.

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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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  • 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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  • 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, to appear.

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  • 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, 2018, to appear.

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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 Computer Society Conference on Computer Vision and Pattern Recognition, 2018, Salt Lake City, to appear.

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  • A. Pumarola, A. Vakhitov, A. Agudo, A. Sanfeliu and F. Moreno-Noguer. PL-SLAM: Real-time monocular visual SLAM with points and lines, 2017 IEEE International Conference on Robotics and Automation, 2017, Singapore, pp. 4503-4508.

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  • C. Torras. Assistent(e)s robòtiqu(e)s: Un punt de confluència entre tecnociència i humanitats. Quadern de les idees, les arts i les lletres, 38(209): 19-21, 2017.

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  • A. Ramisa. Multimodal news article analysis, 26th International Joint Conference on Artificial Intelligence, 2017, Melbourne, Australia, pp. 5136-5140.

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  • A. Colomé and C. Torras. Dual REPS: A generalization of relative entropy policy search exploiting bad experiences. IEEE Transactions on Robotics, 33(4): 978-985, 2017.

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  • G. Canal, G. Alenyà and C. Torras. A taxonomy of preferences for physically assistive robots, 26th IEEE International Symposium on Robot and Human Interactive Communication, 2017, Lisbon, Portugal, pp. 292-297.

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  • P. Jiménez. Software tools for the cognitive development of autonomous robots, 2017 International Workshop on Sense and Scope of Autonomy in Emerging Military and Security Technologies, 2017, Barcelona, pp. 18-32, The Richardson Institute, Lancaster University, UK.

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  • M. Alberich-Carramiñana, B. Elizalde and F. Thomas. New algebraic conditions for the identification of the relative position of two coplanar ellipses. Computer Aided Geometric Design, 54: 35-48, 2017.

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  • A. Suárez, C. Miralles and S. Medina. On the application of CMA-ES to biped walk. Technical Report IRI-TR-17-03, Institut de Robòtica i Informàtica Industrial, CSIC-UPC, 2017.

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  • A. Agudo and F. Moreno-Noguer. DUST: Dual union of spatio-temporal subspaces for monocular multiple object 3D reconstruction, 2017 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2017, Honolulu, USA, pp. 1513-1521, IEEE.

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  • F. Husain, B. Dellen and C. Torras. Scene understanding using deep learning. In Handbook of Neural Computation, 373-382. Elsevier, 2017.

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  • A. Agostini, G. Alenyà, A. Fischbach, H. Scharr, F. Wörgötter and C. Torras. A cognitive architecture for automatic gardening. Computers and Electronics in Agriculture, 138: 69-79, 2017.

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  • I. del Pino, V. Vaquero, B. Masini, J. Solà, F. Moreno-Noguer, A. Sanfeliu and J. Andrade-Cetto. Low resolution lidar-based multi object tracking for driving applications, 3rd Iberian Robotics Conference, 2017, Seville, in Robot 2017: Third Iberian Robotics Conference, Vol 694 of Advances in Intelligent Systems and Computing, pp. 287-298, Springer.

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  • D. Lunni, A. Santamaria-Navarro, R. Rossi, P. Rocco, L. Bascetta and J. Andrade-Cetto. Nonlinear model predictive control for aerial manipulation, 2017 International Conference on Unmanned Aircraft Systems, 2017, Miami, pp. 87-93.

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  • D. Martínez, G. Alenyà, T. Ribeiro, K. Inoue and C. Torras. Relational reinforcement learning for planning with exogenous effects. Journal of Machine Learning Research, 18(78): 1-44, 2017.

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  • D. Martínez, G. Alenyà and C. Torras. Relational reinforcement learning with guided demonstrations. Artificial Intelligence, 247: 295–312, 2017.

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  • G. Chance, A. Jevtić, P. Caleb-Solly and S. Dogramadzi. A quantitative analysis of dressing dynamics for robotic dressing assistance. Frontiers in Robotics and AI, 4(13): 1-14, 2017.

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  • A. Rubio, L. Yu, E. Simo-Serra and F. Moreno-Noguer. Multi-modal embedding for main product detection in fashion, 2017 ICCV Workshop on Computer Vision for Fashion, 2017, Venice, Italy, pp. 2236-2242.

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  • A. Agostini, C. Torras and F. Wörgötter. Efficient interactive decision-making framework for robotic applications. Artificial Intelligence, 247: 187-212, 2017.

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  • G. Chance, P. Caleb-Solly, A. Jevtić and S. Dogramadzi. What's “up”? — Resolving interaction ambiguity through non-visual cues for a robotic dressing assistant, 26th IEEE International Symposium on Robot and Human Interactive Communication, 2017, Lisbon, Portugal, pp. 284-291.

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  • N. Covallero, D. Martínez, G. Alenyà and C. Torras. Planning clearing actions in cluttered scenes by phasing in geometrical constraints, 20th Catalan Conference on Artificial Intelligence, 2017, Deltebre, in Recent Advances in Artificial Intelligence Research and Development, Vol 300 of Frontiers in Artificial Intelligence and Applications, pp. 233-238, 2017, IOS Press.

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  • F. Husain, H. Schulz, B. Dellen, C. Torras and S. Behnke. Combining semantic and geometric features for object class segmentation of indoor scenes. IEEE Robotics and Automation Letters, 2(1): 49-55, 2017.

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  • G. Alenyà, R. Téllez, K. O'Regan and C. Angulo. Guest editorial sensorimotor contingencies for cognitive robotics. IEEE Transactions on Cognitive and Developmental Systems, 9(2): 97-99, 2017.

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  • A. Colomé, S. Foix, G. Alenyà and C. Torras. Reward-weighted GMM and its application to action-selection in robotized shoe dressing, 3rd Iberian Robotics Conference, 2017, Seville, in Robot 2017: Third Iberian Robotics Conference, Vol 694 of Advances in Intelligent Systems and Computing, pp. 141-152, Springer.

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  • A. Agudo, J.M. Martínez, L. Agapito and B. Calvo. Modal space: A physics-based model for sequential estimation of time-varying shape from monocular video. Journal of Mathematical Imaging and Vision, 57(1): 75–98, 2017.

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  • P. Jiménez. Visual grasp point localization, classification and state recognition in robotic manipulation of cloth: An overview. Robotics and Autonomous Systems, 92: 107-125, 2017.

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  • A. Hernandez Ruiz, L. Porzi, S. Rota and F. Moreno-Noguer. 3D CNNs on distance matrices for human action recognition, 25th ACM Conference on Multimedia, 2017, Mountain View, CA. USA., pp. 1087-1095.

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  • M. Villamizar, A. Garrell Zulueta, A. Sanfeliu and F. Moreno-Noguer. Random clustering ferns for multimodal object recognition. Neural Computing and Applications, 28(9): 2445-2460, 2017.

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  • V. Vaquero, G. Ros, F. Moreno-Noguer, A. M. and A. Sanfeliu. Joint coarse-and-fine reasoning for deep optical flow, 2017 IEEE International Conference on Image Processing, 2017, Beijing, pp. 2558-2562.

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  • C. Torras. Els reptes de la robòtica assistencial. Memòries de la Reial Acadèmia de Ciències i Arts de Barcelona: 433-457/66/8, 2017.

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  • A. Agudo and F. Moreno-Noguer. Combining local-physical and global-statistical models for sequential deformable shape from motion. International Journal of Computer Vision, 122(2): 371-387, 2017.

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  • A. Rubio, L. Yu, E. Simo-Serra and F. Moreno-Noguer. Multi-modal fashion product retrieval, 6th Workshop on Vision and Language, 2017, Valencia, pp. 43-45.

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  • L. Porzi, S. Rota-Bulò, A. Penate-Sanchez, E. Ricci and F. Moreno-Noguer. Learning depth-aware deep representations for robotic perception. IEEE Robotics and Automation Letters, 2(2): 468-475, 2017.

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  • A. Agudo and F. Moreno-Noguer. Global model with local interpretation for dynamic shape reconstruction, 2017 IEEE Winter Conference on Applications of Computer Vision, 2017, Santa Rosa (California), USA, pp. 264-272, IEEE.

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  • A. Rubio, L. Yu, E. Simo-Serra and F. Moreno-Noguer. Multi-modal joint embedding for fashion product retrieval, 2017 IEEE International Conference on Image Processing, 2017, Beijing, pp. 400-404.

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  • J. Funke, J. Klein, F. Moreno-Noguer, A. Cardona and M. Cook. TED: A tolerant edit distance for segmentation evaluation. Methods, 115: 119-127, 2017.

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  • S. Bensch, A. Jevtić and T. Hellström. On interaction quality in human-robot interaction, 9th International Conference on Agents and Artificial Intelligence, 2017, Porto, Portugal, pp. 182-189.

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  • V. Vaquero, I. del Pino, F. Moreno-Noguer, J. Solà, A. Sanfeliu and J. Andrade-Cetto. Deconvolutional networks for point-cloud vehicle detection and tracking in driving scenarios, 8th European Conference on Mobile Robots, 2017, Paris, France, pp. 1-7.

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  • F. Moreno-Noguer. 3D human pose estimation from a single image via distance matrix regression, 2017 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2017, Honolulu, USA, pp. 1561-1570, IEEE.

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  • E. Simo-Serra, C. Torras and F. Moreno-Noguer. 3D human pose tracking priors using geodesic mixture models. International Journal of Computer Vision, 122(2): 388-408, 2017.

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  • A. Colomé and C. Torras. Demonstration-free contextualized probabilistic movement primitives, further enhanced with obstacle avoidance, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017, Vancouver, Canada, pp. 3190-3195.

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  • L. Porzi, A. Penate-Sanchez, E. Ricci and F. Moreno-Noguer. Depth-aware convolutional neural networks for accurate 3D pose estimation in RGB-D images, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017, Vancouver, Canada, pp. 5777-5783.

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  • A. Garrell Zulueta, M. Villamizar, F. Moreno-Noguer and A. Sanfeliu. Teaching robot’s proactive behavior using human assistance. International Journal of Social Robotics, 9(2): 231-249, 2017.

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  • M. Arduengo, S.J. Jorgensen, K. Hambuchen, L. Sentis, F. Moreno-Noguer and G. Alenyà. ROS wrapper for real-time multi-person pose estimation with a single camera. Technical Report IRI-TR-17-02, Institut de Robòtica i Informàtica Industrial, CSIC-UPC, 2017.

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  • A. Ramisa, F. Yan, F. Moreno-Noguer and K. Mikolajczyk. The BreakingNews dataset, 6th Workshop on Vision and Language, 2017, Valencia, pp. 38-29.

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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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