Interactive object classification using sensorimotor contingencies

Understanding and representing objects and their function is a challenging task. Objects we manipulate in our daily activities can be described and categorized in various ways according to their properties or affordances, depending also on our perception of those. In this work, we are interested in representing the knowledge acquired through interaction with objects, describing these in terms of action-effect relations, i.e. sensorimotor contingencies, rather than static shape or appearance representations. We demonstrate how a robot learns sensorimotor contingencies through pushing using a probabilistic model. We show how functional categories can be discovered and how entropy-based action selection can improve object classification.

[1]  Connor Schenck,et al.  Grounding semantic categories in behavioral interactions: Experiments with 100 objects , 2014, Robotics Auton. Syst..

[2]  Niklas Bergström,et al.  Scene Understanding through Autonomous Interactive Perception , 2011, ICVS.

[3]  A. Noë,et al.  A sensorimotor account of vision and visual consciousness. , 2001, The Behavioral and brain sciences.

[4]  Silvio Savarese,et al.  3D generic object categorization, localization and pose estimation , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[5]  Paul F. M. J. Verschure,et al.  The acquisition of intentionally indexed and object centered affordance gradients: A biomimetic controller and mobile robotics benchmark , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Danica Kragic,et al.  Active 3D scene segmentation and detection of unknown objects , 2010, 2010 IEEE International Conference on Robotics and Automation.

[7]  Christopher W. Geib,et al.  The meaning of action: a review on action recognition and mapping , 2007, Adv. Robotics.

[8]  Jan Peters,et al.  Model learning for robot control: a survey , 2011, Cognitive Processing.

[9]  Bernt Schiele,et al.  Analyzing appearance and contour based methods for object categorization , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[10]  Mark Steedman,et al.  Object-Action Complexes: Grounded abstractions of sensory-motor processes , 2011, Robotics Auton. Syst..

[11]  P. Verschure,et al.  A biologically based model for the integration of sensory–motor contingencies in rules and plans: A prefrontal cortex based extension of the Distributed Adaptive Control architecture , 2011, Brain Research Bulletin.

[12]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[13]  Alexander Stoytchev,et al.  Behavior-Grounded Representation of Tool Affordances , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[14]  J. Gibson.  The Ecological Approach to Visual Perception , 1979 .

[15]  Alexander Maye,et al.  A discrete computational model of sensorimotor contingencies for object perception and control of behavior , 2011, 2011 IEEE International Conference on Robotics and Automation.

[16]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[17]  Emre Ugur,et al.  Goal emulation and planning in perceptual space using learned affordances , 2011, Robotics Auton. Syst..

[18]  David L. Faigman,et al.  Human category learning. , 2005, Annual review of psychology.

[19]  Danica Kragic,et al.  Visual object-action recognition: Inferring object affordances from human demonstration , 2011, Comput. Vis. Image Underst..

[20]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[21]  Alexander Stoytchev,et al.  Some Basic Principles of Developmental Robotics , 2009, IEEE Transactions on Autonomous Mental Development.

[22]  Giulio Sandini,et al.  Learning about objects through action - initial steps towards artificial cognition , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[23]  Guillaume Bouchard,et al.  Hierarchical part-based visual object categorization , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[24]  Giorgio Metta,et al.  Better Vision through Manipulation , 2003, Adapt. Behav..