Research Project

PAU+: Perception and Action in Robotics Problems with Large State Spaces

Type

National Project

Start Date

01/01/2012

End Date

30/06/2015

Project Code

DPI2011-27510

Project illustration

Staff

Project Description

Project web page.

Summary:
The project goal is to contribute to the theoretical foundations of computational models of perception and action and their application to the solution of challenging robotics problems with large state spaces. The majority of the methods and techniques studied in this project have as pillars Bayesian estimation on one side, and geometrical models in robotics and computer vision on the other.

The application domains of the project are mobile robot mapping and navigation, and perception and manipulation of deformable objects. Although very different in nature, these two applications share many similarities with respect to the formalism of perception, estimation and exploration. The most important aspect is that both types of systems are commonly represented with very large state spaces.

This project is the continuation of the PAU project (DPI2008-06022), in which we mainly studied, for the abovementioned application domains, novel uncertainty parameterizations that allow efficient inference, new probabilistic hypothesis testing strategies with respect to information load, active exploration paradigms for scene and object reconstruction, and new algorithms for rigid and non-rigid object reconstruction from single images.

During the course of PAU, uncertainty in motion and sensor models has been treated with the aid of stochastic filtering methods. We will still explore and extend these mechanisms for efficient inference, but also concentrate on smoothing techniques over Bayes networks, which for large-scale systems show better convergence properties and behave also better with respect to nonlinear approximations.

Another lesson learned from our research in PAU is that we need imaginative ways to parameterize or reduce the solution space; for instance, by using deformation modes in the case of deformable objects, or by state and constraint pruning with regards to information content in the case of SLAM. In PAU+ we will extend these ideas of reduced state representation from perception to action, seeking optimal navigation paths in the ensuing sparse Bayes graph, or by characterizing a small set of distinctive deformation configurations for manipulation planning. The addition of a foreign Ramón y Cajal researcher to the group brings fresh ideas with regards to these higher level semantic representations in the context of action recognition and replication.

Specific expected scientific contributions for PAU+ include efficient online probabilistic inference algorithms on graphical models with applications to simultaneous localization and mapping (SLAM) and for rigid shape estimation; their extension to non-rigid object reconstruction and pose estimation using both geometric and appearance constraints; as well as robust pose estimation for uncalibrated vision systems. At a higher level of abstraction, we foresee the creation of novel optimal navigation strategies over belief networks, action recognition in manipulation tasks from video sequences, and learning manipulation skills through demonstration.

The two applications, outdoor mobile robot mapping and navigation, and shape recovery and manipulation of deformable objects are commercially highly relevant robotics technologies, as endorsed by our EPOs. With the development of such technologies, the project will contribute to more flexible and general use of robots in industrial processes, as well as more robust service robots able to navigate and cooperate in previously unknown and unstructured environments.

Project Publications

Journal Publications

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

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  • G. Sanromà, A. Penate-Sanchez, R. Alquézar Mancho, F. Serratosa, F. Moreno-Noguer, J. Andrade-Cetto and M.A. González. MSClique: Multiple structure discovery through the maximum weighted clique problem. PLOS One, 11(1): e0145846, 2016.

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  • C. Torras. Service robots for citizens of the future. European Review, 24(1): 17-30, 2016.

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  • L. Rozo, S. Calinon, D. Caldwell, P. Jiménez and C. Torras. Learning physical collaborative robot behaviors from human demonstrations. IEEE Transactions on Robotics, 32(3): 513-527, 2016.

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  • A. Santamaria-Navarro, E.H. Teniente, M. Morta and J. Andrade-Cetto. Terrain classification in complex three-dimensional outdoor environments. Journal of Field Robotics, 32(1): 42-60, 2015.

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  • E. Simo-Serra, C. Torras and F. Moreno-Noguer. DaLI: Deformation and light invariant descriptor. International Journal of Computer Vision, 115(2): 136–154, 2015.

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  • R. Galego, A.A. Ortega, R. Ferreira, A.J. Bernardino Malheiro, J. Andrade-Cetto and J. Gaspar. Uncertainty analysis of the DLT-lines calibration algorithm for cameras with radial distortion. Computer Vision and Image Understanding, 140: 115-126, 2015.

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  • J. Vallvé and J. Andrade-Cetto. Potential information fields for mobile robot exploration. Robotics and Autonomous Systems, 69: 68-79, 2015.

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  • E. Serradell, M.A. Pinheiro, R. Sznitman, J. Kybic, F. Moreno-Noguer and P. Fua. Non-rigid graph registration using active testing search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3): 625-638, 2015.

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  • D. Martínez, G. Alenyà and C. Torras. Planning robot manipulation to clean planar surfaces. Engineering Applications of Artificial Intelligence, 39: 23-32, 2015.

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  • F. Husain, B. Dellen and C. Torras. Consistent depth video segmentation using adaptive surface models. IEEE Transactions on Cybernetics, 45(2): 266-278, 2015.

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  • A.A. Ortega, M. Silva, E.H. Teniente, R. Ferreira, A.J. Bernardino Malheiro, J. Gaspar and J. Andrade-Cetto. Calibration of an outdoor distributed camera network with a 3D point cloud. Sensors, 14(8): 13708-13729, 2014.

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  • G. Alenyà, S. Foix and C. Torras. ToF cameras for active vision in robotics. Sensors and Actuators A: Physical, 218: 10-22, 2014.

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  • A. Ramisa, G. Alenyà, F. Moreno-Noguer and C. Torras. Learning RGB-D descriptors of garment parts for informed robot grasping. Engineering Applications of Artificial Intelligence, 35: 246-258, 2014.

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  • F. Husain, B. Dellen and C. Torras. Robust surface tracking in range image sequences. Digital Signal Processing, 35: 37-44, 2014.

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  • G. Alenyà, S. Foix and C. Torras. Using ToF and RGBD cameras for 3D robot perception and manipulation in human environments. Intelligent Service Robotics, 7(4): 211-220, 2014.

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  • W. Kazmi, S. Foix, G. Alenyà and H.J. Andersen. Indoor and outdoor depth imaging of leaves with time-of-flight and stereo vision sensors: Analysis and comparison. ISPRS Journal of Photogrammetry and Remote Sensing, 88: 128-146, 2014.

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  • G. Alenyà, B. Dellen, S. Foix and C. Torras. Robotized plant probing: Leaf segmentation utilizing time-of-flight data. IEEE Robotics and Automation Magazine, 20(3): 50-59, 2013.

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  • L. Rozo, P. Jiménez and C. Torras. A robot learning from demonstration framework to perform force-based manipulation tasks. Intelligent Service Robotics, 6(1): 33-51, 2013.

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  • F. Moreno-Noguer and P. Fua. Stochastic exploration of ambiguities for nonrigid shape recovery. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(2): 463-475, 2013.

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  • A. Penate-Sanchez, J. Andrade-Cetto and F. Moreno-Noguer. Exhaustive linearization for robust camera pose and focal length estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(10): 2387-2400, 2013.

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  • R. Valencia, M. Morta, J. Andrade-Cetto and J.M. Porta. Planning reliable paths with Pose SLAM. IEEE Transactions on Robotics, 29(4): 1050-1059, 2013.

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  • M. Villamizar, J. Andrade-Cetto, A. Sanfeliu and F. Moreno-Noguer. Bootstrapping boosted random Ferns for discriminative and efficient object classification. Pattern Recognition, 45(9): 3141-3153, 2012.

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Conference Publications

  • E. Simo-Serra, S. Fidler, F. Moreno-Noguer and R. Urtasun. Neuroaesthetics in fashion: Modeling the perception of fashionability, 2015 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015, Boston.

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  • E. Simo-Serra, C. Torras and F. Moreno-Noguer. Lie algebra-based kinematic prior for 3D human pose tracking, 14th IAPR International Conference on Machine Vision Applications, 2015, Tokyo, Japan, pp. 394-397.

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  • A. Santamaria-Navarro, J. Solà and J. Andrade-Cetto. High-frequency MAV state estimation using low-cost inertial and optical flow measurement units, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2015, Hamburg, pp. 1864-1871.

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  • A. Agudo and F. Moreno-Noguer. Simultaneous pose and non-rigid shape with particle dynamics, 2015 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015, Boston, pp. 2179-2187.

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  • D. Martínez, G. Alenyà and C. Torras. V-MIN: Efficient reinforcement learning through demonstrations and relaxed reward demands, 29th AAAI Conference on Artificial Intelligence, 2015, Austin, Texas, pp. 2857-2863.

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  • J. Vallvé and J. Andrade-Cetto. Active Pose SLAM with RRT*, 2015 IEEE International Conference on Robotics and Automation, 2015, Seattle, pp. 2167-2173.

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  • A. Rubio, M. Villamizar, L. Ferraz, A. Penate-Sanchez, A. Ramisa, E. Simo-Serra, A. Sanfeliu and F. Moreno-Noguer. Efficient monocular pose estimation for complex 3D models, 2015 IEEE International Conference on Robotics and Automation, 2015, Seattle, pp. 1397-1402.

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  • A.A. Ortega and J. Andrade-Cetto. Dynamic object detection fusing LIDAR data and images, X Taller de Procesamiento de Imágenes, 2014, Guanajuato México.

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  • E. Simo-Serra, C. Torras and F. Moreno-Noguer. Geodesic finite mixture models, 2014 British Machine Vision Conference, 2014, .

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  • M.G. Jadidi, J. Valls Miró, R. Valencia and J. Andrade-Cetto. Exploration on continuous Gaussian process frontier maps, 2014 IEEE International Conference on Robotics and Automation, 2014, Hong Kong, pp. 6077-6082.

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  • X. Solé, A. Ramisa and C. Torras. Evaluation of random forests on large-scale classification problems using a bag-of-visual-words representation, 17th Catalan Conference on Artificial Intelligence, 2014, Barcelona, in Artificial Intelligence Research and Development, Vol 269 of Frontiers in Artificial Intelligence and Applications, pp. 273-276, 2014, IOS Press.

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  • J. Vallvé and J. Andrade-Cetto. Dense entropy decrease estimation for mobile robot exploration, 2014 IEEE International Conference on Robotics and Automation, 2014, Hong Kong, pp. 6083-6089.

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  • C. Torras. Robot manipulation in human environments: Challenges for learning algorithms, 14081 Dagstuhl Seminar, 2014, Dagstuhl, Germany, in Robots Learning from Experiences, Vol 4-2 of Dagstuhl Reports, pp. 99-101, Schloss Dagstuhl.

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  • R. Valencia, J. Saarinen, H. Andreasson, J. Vallvé, J. Andrade-Cetto and A. Llilienthal. Localization in highly dynamic environments using dual-timescale NDT-MCL, 2014 IEEE International Conference on Robotics and Automation, 2014, Hong Kong, pp. 3956-3962.

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  • L. Ferraz, X. Binefa and F. Moreno-Noguer. Very fast solution to the PnP problem with algebraic outlier rejection, 2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2014, Columbus, pp. 501-508.

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  • M. Villamizar, A. Sanfeliu and F. Moreno-Noguer. Fast online learning and detection of natural landmarks for autonomous aerial robots, 2014 IEEE International Conference on Robotics and Automation, 2014, Hong Kong, pp. 4996-5003.

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  • L. Ferraz, X. Binefa and F. Moreno-Noguer. Leveraging feature uncertainty in the PnP problem, 2014 British Machine Vision Conference, 2014, .

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  • E. Trulls Fortuny, S. Tsogkas, I. Kokkinos, A. Sanfeliu and F. Moreno-Noguer. Segmentation-aware deformable part models, 2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2014, Columbus, pp. 168-175.

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  • E. Simo-Serra, S. Fidler, F. Moreno-Noguer and R. Urtasun. A high performance CRF model for clothes parsing, 12th Asian Conference on Computer Vision, 2014, Singapore, in Computer Vision - ACCV 2014, Vol 9005 of Lecture Notes in Computer Science, pp. 64-81, 2015, Springer.

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  • A. Penate-Sanchez, F. Moreno-Noguer, J. Andrade-Cetto and F. Fleuret. LETHA: Learning from high quality inputs for 3D pose estimation in low quality images, 2nd International Conference on 3D Vision, 2014, Tokyo, pp. 517-524.

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  • A. Colomé and C. Torras. Dimensionality reduction and motion coordination in learning trajectories with dynamic movement primitives, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014, Chicago, pp. 1414-1420.

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  • D. Martínez, G. Alenyà and C. Torras. Finding safe policies in model-based active learning, 2014 IROS Machine Learning in Planning and Control of Robot Motion Workshop, 2014, Chicago, pp. 1-6.

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  • F. Husain, A. Colomé, B. Dellen, G. Alenyà and C. Torras. Realtime tracking and grasping of a moving object from range video, 2014 IEEE International Conference on Robotics and Automation, 2014, Hong Kong, pp. 2617-2622.

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  • F. Husain, B. Dellen and C. Torras. Recognizing point clouds using conditional random fields, 22nd International Conference on Pattern Recognition, 2014, Stockhom, Sweden, pp. 4257-4262, IEEE Computer Society.

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  • D. Martínez, G. Alenyà and C. Torras. Planning surface cleaning tasks by learning uncertain drag actions outcomes, 2013 ICAPS Workshop on Planning and Robotics, 2013, Rome, pp. 106-111.

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  • M.A. Pinheiro, R. Sznitman, E. Serradell, J. Kybic, F. Moreno-Noguer and P. Fua. Active testing search for point cloud matching, 23rd International Conference on Information Processing in Medical Imaging, 2013, Asilimar, USA, in Information Processing in Medical Imaging, Vol 7917 of Lecture Notes in Computer Science, pp. 572-583, Springer-Verlag.

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  • M.G. Jadidi, J. Valls Miró, R. Valencia, J. Andrade-Cetto and G. Dissanayake. Exploration in information distribution maps, 2013 RSS Workshop on Robotic Exploration, Monitoring, and Information Collection, 2013, Berlin, Germany, pp. 1-8.

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  • J. Vallvé and J. Andrade-Cetto. Mobile robot exploration with potential information fields, 6th European Conference on Mobile Robots, 2013, Barcelona, pp. 222-227.

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  • A.A. Ortega, R. Galego, R. Ferreira, A.J. Bernardino Malheiro, J. Gaspar and J. Andrade-Cetto. Estimation of camera calibration uncertainty using LIDAR data, 6th European Conference on Mobile Robots, 2013, Barcelona, pp. 361-366.

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  • E.H. Teniente and J. Andrade-Cetto. HRA*: Hybrid randomized path planning for complex 3D environments, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013, Tokyo, pp. 1766-1771.

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  • A. Colomé, D. Pardo, G. Alenyà and C. Torras. External force estimation during compliant robot manipulation, 2013 IEEE International Conference on Robotics and Automation, 2013, Karlsruhe, pp. 3535-3540.

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  • A. Penate-Sanchez, E. Serradell, J. Andrade-Cetto and F. Moreno-Noguer. Simultaneous pose, focal length and 2D-to-3D correspondences from noisy observations, 2013 British Machine Vision Conference, 2013, Bristol, UK, pp. 82.1-82.11.

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  • M.G. Jadidi, J. Valls Miró, R. Valencia, J. Andrade-Cetto and G. Dissanayake. Exploration using an information-based reaction-diffusion process, 2013 Australasian Conference on Robotics and Automation, 2013, Sydney, pp. 1-10.

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  • A. Ramisa, G. Alenyà, F. Moreno-Noguer and C. Torras. FINDDD: A fast 3D descriptor to characterize textiles for robot manipulation, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013, Tokyo, pp. 824-830.

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  • E. Trulls Fortuny, I. Kokkinos, A. Sanfeliu and F. Moreno-Noguer. Dense segmentation-aware descriptors, 2013 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2013, Portland, pp. 2890-2897.

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  • L. Rozo, S. Calinon, D. Caldwell, P. Jiménez and C. Torras. Learning collaborative impedance-based robot behaviors, 27th AAAI Conference on Artificial Intelligence, 2013, Bellevue, pp. 1422-1428.

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  • L. Rozo, P. Jiménez and C. Torras. Force-based robot learning of pouring skills using parametric hidden Markov models, 9th International Workshop on Robot Motion and Control, 2013, Wasowo, Poland, pp. 227-232.

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  • C. Torras. From the Turing test to science fiction: The challenges of social robotics, 16th Catalan Conference on Artificial Intelligence, 2013, Vic, Spain, in Artificial Intelligence Research and Development, Vol 256 of Frontiers in Artificial Intelligence and Applications, pp. 5-7, 2013, IOS Press.

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  • A. Santamaria-Navarro and J. Andrade-Cetto. Uncalibrated image-based visual servoing, 2013 IEEE International Conference on Robotics and Automation, 2013, Karlsruhe, pp. 5247-5252.

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  • E. Simo-Serra, A. Quattoni, C. Torras and F. Moreno-Noguer. A joint model for 2D and 3D pose estimation from a single image, 2013 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2013, Portland, pp. 3634--3641.

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  • A. Colomé, G. Alenyà and C. Torras. Handling high parameter dimensionality in reinforcement learning with dynamic motor primitives, 2013 ICRA Workshop on Novel Methods for Learning and Optimization of Control Policies and Trajectories for Robotics, 2013, Karlsruhe, Germany.

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  • S. Foix, S. Kriegel, S. Fuchs, G. Alenyà and C. Torras. Information-gain view planning for free-form object reconstruction with a 3D ToF camera, 14th International Conference on Advanced Concepts for Intelligent Vision Systems, 2012, Brno, Czech Republic, in Advanced Concepts for Intelligent Vision Systems, Vol 7517 of Lecture Notes in Computer Science, pp. 36-47, 2012, Springer.

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  • E. Simo-Serra, A. Ramisa, G. Alenyà, C. Torras and F. Moreno-Noguer. Single image 3D human pose estimation from noisy observations, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2012, Providence, pp. 2673-2680, IEEE Press.

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  • M. Villamizar, A. Garrell Zulueta, A. Sanfeliu and F. Moreno-Noguer. Online human-assisted learning using random ferns, 21st International Conference on Pattern Recognition, 2012, Tsukuba, Japan, pp. 2821-2824, IEEE Computer Society.

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  • P. Monsó, G. Alenyà and C. Torras. POMDP approach to robotized clothes separation, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012, Vilamoura, Portugal, pp. 1324-1329, IEEE.

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  • W. Kazmi, S. Foix and G. Alenyà. Plant leaf imaging using time of flight camera under sunlight, shadow and room conditions, 2012 IEEE International Symposium on Robotic and Sensors Environments, 2012, Magdeburg, Germany, pp. 192-197, IEEE.

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  • E. Trulls Fortuny, A. Sanfeliu and F. Moreno-Noguer. Spatiotemporal descriptor for wide-baseline stereo reconstruction of non-rigid and ambiguous scenes, 12th European Conference on Computer Vision, 2012, Florence, Italy, in Computer Vision, Vol 7574 of Lecture Notes in Computer Science, pp. 441-454, 2012, Springer.

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  • G. Alenyà, A. Ramisa, F. Moreno-Noguer and C. Torras. Characterization of textile grasping experiments, 2012 ICRA Workshop on Conditions for Replicable Experiments and Performance Comparison in Robotics Research, 2012, St Paul, Minnesota, USA, pp. 1-6.

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  • E. Serradell, P. Glowacki, K. Jan, F. Moreno-Noguer and P. Fua. Robust non-rigid registration of 2D and 3D graphs, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2012, Providence, pp. 996-1003, IEEE Press.

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  • E. Serradell, K. Jan, F. Moreno-Noguer and P. Fua. Robust elastic 2D/3D geometric graph matching, 2012 SPIE Medical Imaging: Image Processing, 2012, San Diego CA, USA, Vol 8314 of Proceedings of the SPIE, 2012, SPIE.

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  • A. Ramisa, G. Alenyà, F. Moreno-Noguer and C. Torras. Using depth and appearance features for informed robot grasping of highly wrinkled clothes, 2012 IEEE International Conference on Robotics and Automation, 2012, St. Paul, Minnesota, USA, pp. 1703-1708, IEEE.

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  • R. Valencia, J. Valls Miró, G. Dissanayake and J. Andrade-Cetto. Active Pose SLAM, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012, Vilamoura, Portugal, pp. 1885-1891, IEEE.

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Other Publications

  • E. Trulls Fortuny, I. Kokkinos, A. Sanfeliu and F. Moreno-Noguer. Dense segmentation-aware descriptors. In Dense Image Correspondences for Computer Vision, 83-107. Springer, 2016.

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  • G. Alenyà, S. Foix and C. Torras. ToF cameras for eye-in-hand robotics. In Optical Imaging Devices: New Technologies and Applications, 117-147. CRC Press - Francis Taylor Group, 2015.

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  • Y. Isleyici and G. Alenyà. Teaching grasping points using natural movements. Technical Report IRI-TR-14-01, Institut de Robòtica i Informàtica Industrial, CSIC-UPC, 2014.

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