%0 Conference Paper %B Humanoid Robots (Humanoids), 2015 IEEE-RAS 15th International Conference on %D 2015 %T Tombatossals: A humanoid torso for autonomous sensor-based tasks %A Felip, Javier %A Angel J Duran %A Antonelli, Marco %A Morales, Antonio %A Angel P. del Pobil %B Humanoid Robots (Humanoids), 2015 IEEE-RAS 15th International Conference on %I IEEE %G eng %0 Conference Proceedings %B 13th IEEE-RAS, International Conference on Humanoid Robots, HUMANOIDS %D 2013 %T Contact Localization through Robot and Object Motion from Point Clouds %A Jose Bernabe %A Javier Felip %A Angel P. del Pobil %A Antonio Morales %K Physical interaction %K Tactile perception %K visual perception %X

Abstract: In this paper we address the problem of detecting contacts between a robot hand and an object during the approach and execution phases of manipulation tasks as a complement of touch perception. The motion produced on objects by unnoticed contacts is exploited to trigger the contact localization. Our method combines robot motion with tracking of target objects by means of point cloud to probabilistically estimate the location of contacts on a occupancy grid map. No previous knowledge on the object shape is required. The proposed approach is implemented on a real platform and experimentally validated in several cases.

%B 13th IEEE-RAS, International Conference on Humanoid Robots, HUMANOIDS %I IEEE %C Atlanta, GA, USA %8 10/2013 %G eng %0 Book Section %B Designing Intelligent Robots: Reintegrating AI %D 2013 %T Integration of Visuomotor Learning, Cognitive Grasping and Sensor-Based Physical Interaction in the UJI Humanoid Torso %A Angel P. del Pobil %A Angel J Duran %A Marco Antonelli %A Javier Felip %A Antonio Morales %A M. Prats %A Eris Chinellato %B Designing Intelligent Robots: Reintegrating AI %I AAAI %V SS-13-04 %P pp. 6-11 %@ 978-1-57735-601-1 %G eng %0 Conference Paper %B 12th IEEE-RAS International Conference on Humanoid Robots %D 2012 %T Contact-based blind grasping of unknown objects %A Javier Felip %A Jose Bernabe %A Antonio Morales %B 12th IEEE-RAS International Conference on Humanoid Robots %8 Nov. %@ 978-1-4673-1369-8 %G eng %0 Journal Article %J Robotics and Autonomous Systems %D 2012 %T Manipulation primitives: A paradigm for abstraction and execution of grasping and manipulation tasks %A Javier Felip %A Laaksonen, J. %A Antonio Morales %A V. Kyrki %B Robotics and Autonomous Systems %G eng %R 10.1016/j.robot.2012.11.010 %0 Conference Paper %B 12th IEEE-RAS International Conference on Humanoid Robots %D 2012 %T Simulation of robot dynamics for grasping and manipulation tasks %A Beatriz León %A Javier Felip %A Higinio Martí %A Antonio Morales %B 12th IEEE-RAS International Conference on Humanoid Robots %8 Nov. %@ 978-1-4673-1369-8 %G eng %0 Journal Article %J Neurocomputing %D 2011 %T The Dorso-medial visual stream: From neural activation to sensorimotor interaction %A Eris Chinellato %A Beata J. Grzyb %A Nicoletta Marzocchi %A A. Bosco %A Patrizia Fattori %A Angel P. del Pobil %K Bio-inspired systems %X

The posterior parietal cortex of primates, and more exactly areas of the dorso-medial visual stream, are able to encode the peripersonal space of a subject in a way suitable for gathering visual information and contextually performing purposeful gazing and arm reaching movements. Such sensorimotor knowledge of the environment is not explicit, but rather emerges through the interaction of the subject with nearby objects. In this work, single-cell data regarding the activation of primate dorso-medial stream neurons during gazing and reaching movements is studied, with the purpose of discovering meaningful pattern useful for modeling purposes. The outline of a model of the mechanisms which allow humans and other primates to build dynamical representations of their peripersonal space through active interaction with nearby objects is proposed, and a detailed description of how to employ the results of the data analysis in the model is offered. The application of the model to robotic systems will allow artificial agents to improve their skills in exploring the nearby space, and will at the same time constitute a way to validate modeling assumptions.

%B Neurocomputing %V 74 %P 1203 - 1212 %G eng %U http://www.sciencedirect.com/science/article/pii/S0925231210004212 %R 10.1016/j.neucom.2010.07.029 %0 Conference Paper %B Computational Intelligence for Visual Intelligence (CIVI), 2011 IEEE Workshop on %D 2011 %T Hierarchical object recognition inspired by primate brain mechanisms %A Eris Chinellato %A Javier Felip %A Beata J. Grzyb %A Antonio Morales %A Angel P. del Pobil %K brain %K Estimation %K Grasping %K hierarchical object recognition %K Image color analysis %K multimodal integration %K mutual projection %K neurophysiology %K neuroscience hypothesis %K object recognition %K object weight estimation %K primate brain mechanism %K real robot %K robot vision %K Robots %K Shape %K visual processing %K Visualization %K visuomotor behavior %B Computational Intelligence for Visual Intelligence (CIVI), 2011 IEEE Workshop on %G eng %R 10.1109/CIVI.2011.5955017 %0 Conference Paper %B Robotics and Automation (ICRA), 2011 IEEE International Conference on %D 2011 %T Mind the gap - robotic grasping under incomplete observation %A J. Bohg %A M. Johnson-Roberson %A Beatriz León %A Javier Felip %A Gratal, X. %A N Bergstrom %A Danica Kragic %A Antonio Morales %K Approximation methods %K collision-free movements %K gap robotic grasping %K Grasping %K Image reconstruction %K incomplete observation %K manipulator kinematics %K mesh generation %K mesh reconstruction %K object shape prediction %K Planning %K Robots %K Shape %K Surface reconstruction %B Robotics and Automation (ICRA), 2011 IEEE International Conference on %G eng %R 10.1109/ICRA.2011.5980354 %0 Conference Paper %B IEEE International Conference on Robotics and Automation (ICRA) %D 2011 %T Mind the gap - robotic grasping under incomplete observation %A Bohg, Jeannette %A M. Johnson-Roberson %A Beatriz León %A Javier Felip %A Gratal, X %A N Bergstrom %A Danica Kragic %A Antonio Morales %K collision-free movements %K gap robotic grasping %K incomplete observation %K manipulator kinematics %K mesh generation %K mesh reconstruction %K object shape prediction %K Robots %B IEEE International Conference on Robotics and Automation (ICRA) %C Shanghai, China %G eng %U http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=5980354 %R 10.1109/ICRA.2011.5980354 %0 Conference Paper %B Robotics and Automation (ICRA), 2010 IEEE International Conference on %D 2010 %T Embodiment independent manipulation through action abstraction %A Laaksonen, J. %A Javier Felip %A Antonio Morales %A V. Kyrki %K abstraction architecture %K Automata %K embodiment independent manipulation %K failure detection %K Hardware %K Humans %K imperfect environment knowledge %K Intelligent robots %K knowledge based systems %K knowledge transfer %K manipulator kinematics %K manipulator platforms %K robot programming %K Robot sensing systems %K Robotics and automation %K Robots %K Sensor phenomena and characterization %K sensor-based control %K sensors %K service tasks %K task analysis %K Uncertainty %K USA Councils %B Robotics and Automation (ICRA), 2010 IEEE International Conference on %G eng %R 10.1109/ROBOT.2010.5509153 %0 Book Section %B Bioinspired Applications in Artificial and Natural Computation, LNCS 5602 %D 2009 %T Eye-Hand Coordination for Reaching in Dorsal Stream Area {V6A}: Computational Lessons %A Eris Chinellato %A Beata J. Grzyb %A Nicoletta Marzocchi %A A. Bosco %A Patrizia Fattori %A Angel P. del Pobil %E J. Mira %E J. M. Ferrandez %E J.R. Alvarez Sánchez %E F. de la Paz %E J. Toledo %B Bioinspired Applications in Artificial and Natural Computation, LNCS 5602 %I Springer %P 304–313 %G eng %R 10.1007/978-3-642-02267-8_33 %0 Conference Paper %B Intelligent Robots and Systems. IROS 2009. IEEE/RSJ International Conference on %D 2009 %T Robust sensor-based grasp primitive for a three-finger robot hand %A Javier Felip %A Antonio Morales %K contact-based sensor %K control law %K Feedback %K Force sensors %K grippers %K Intelligent robots %K Intelligent sensors %K Mobile robots %K naive grasp controller %K robot grasping %K Robot sensing systems %K Robust control %K robust sensor-based grasp primitive %K Robustness %K sensors %K Shape %K three-finger robot hand %K Uncertainty %B Intelligent Robots and Systems. IROS 2009. IEEE/RSJ International Conference on %G eng %R 10.1109/IROS.2009.5354760 %0 Book Section %B Bioinspired Applications in Artificial and Natural Computation, LNCS 5602 %D 2009 %T Toward an Integrated Visuomotor Representation of the Peripersonal Space %A Eris Chinellato %A Beata J. Grzyb %A Patrizia Fattori %A Angel P. del Pobil %E J. Mira %E J. M. Ferrandez %E J.R. Alvarez Sánchez %E F. de la Paz %E J. Toledo %B Bioinspired Applications in Artificial and Natural Computation, LNCS 5602 %P 314–323 %G eng %R 10.1007/978-3-642-02267-8_34 %0 Conference Paper %B 8th Conference on Autonomous Robot Systems and Competitions %D 2008 %T The 2007 Spanish Humanoids Competition: From the Winners Point of View %A García, Juan Carlos %A Javier Felip %A P.J. Sanz %K humanoid robot %K learning %K robot competitions %B 8th Conference on Autonomous Robot Systems and Competitions %C Aveiro (Portugal) %8 04/2008 %@ 978-972-96895-3-6 %G eng %0 Conference Paper %B Intelligent Robots and Systems, 2004. (IROS 2004). Proceedings. 2004 IEEE/RSJ International Conference on %D 2004 %T An active learning approach for assessing robot grasp reliability %A Antonio Morales %A Eris Chinellato %A Fagg, A.H. %A Angel P. del Pobil %K active learning approach %K Costs %K Grasping %K Haptic interfaces %K Intelligent robots %K Laboratories %K learning (artificial intelligence) %K manipulators %K motor control %K Motor drives %K online estimation %K Reliability %K reliability assessment capabilities %K robot grasp reliability %K Robot sensing systems %K Torso %K Training data %K Uncertainty %K visually-guided grasp selection %X

Learning techniques in robotic grasping applications have usually been concerned with the way a hand approaches to an object, or with improving the motor control of manipulation actions. We present an active learning approach devised to face the problem of visually-guided grasp selection. We want to choose the best hand configuration for grasping a particular object using only visual information. Experimental data from real grasping actions is used, and the experience gathering process is driven by an on-line estimation of the reliability assessment capabilities of the system. The goal is to improve the selection skills of the grasping system, minimizing at the same time the cost and duration of the learning process.

%B Intelligent Robots and Systems, 2004. (IROS 2004). Proceedings. 2004 IEEE/RSJ International Conference on %G eng %R 10.1109/IROS.2004.1389399 %F grasping, learning %0 Conference Paper %B Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on %D 2003 %T Experimental prediction of the performance of grasp tasks from visual features %A Antonio Morales %A Eris Chinellato %A Fagg, A.H. %A Angel P. del Pobil %K adaptive behavior %K Barrett hand %K dexterous manipulators %K estimation rule %K feature extraction %K Geometry %K grasp configuration %K Grasping %K hand kinematics %K humanoid robot %K Humans %K Image reconstruction %K Intelligent robots %K Kinematics %K Laboratories %K manipulator kinematics %K object image %K performance prediction %K prediction theory %K Reliability %K Robot sensing systems %K robot vision %K Robustness %K Service robots %K three finger grasps %K unmodeled objects %K visual features %K visually guided grasping %X

This paper deals with visually guided grasping of unmodeled objects for robots which exhibit an adaptive behavior based on their previous experiences. Nine features are proposed to characterize three-finger grasps. They are computed from the object image and the kinematics of the hand. Real experiments on a humanoid robot with a Barrett hand are carried out to provide experimental data. This data is employed by a classification strategy, based on the k-nearest neighbour estimation rule, to predict the reliability of a grasp configuration in terms of five different performance classes. Prediction results suggest the methodology is adequate.

%B Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on %G eng %R 10.1109/IROS.2003.1249685