Robot learning of upper-body human motion by active imitation

This paper presents a general architecture that allows a humanoid robot to imitate upper-body movements of a human demonstrator. This architecture integrates a mechanism to memorize novel behaviours executed by a human demonstrator, with a module to recognize and generate its own interpretation of already observed behaviours. Our imitator includes three biologically plausible components: i) an attention mechanism to autonomously extract relevant information from the visual input; ii) a supra-modal representation of the motion of observed body parts to map visual and motor domains; and iii) an active imitation module which involves the motor systems in the behaviour recognition process. Experimental results with a real humanoid robot demonstrate the ability of the proposed architecture to acquire novel behaviours and to recognize and reproduce previously memorized ones

[1]  Kok Kiong Tan,et al.  Task-oriented developmental learning for humanoid robots , 2005, IEEE Transactions on Industrial Electronics.

[2]  José Santos-Victor,et al.  Visual learning by imitation with motor representations , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[4]  A. Bandera,et al.  A Grid-based Approach to the Body Correspondence Problem in Robot Learning by Imitation , 2006 .

[5]  Francisco Sandoval Hernández,et al.  Real-time human motion analysis for human-robot interaction , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Christof Koch,et al.  Feature combination strategies for saliency-based visual attention systems , 2001, J. Electronic Imaging.

[7]  Chrystopher L. Nehaniv,et al.  Imitation as a Dual-Route Process Featuring Predictive and Learning Components: A Biologically Plausible Computational Model , 2002 .

[8]  Jean-Christophe Terrillon,et al.  Comparative Performance of Different Chrominance Spaces for Color Segmentation and Detection of Human Faces in Complex Scene Images , 1999 .

[9]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Yiannis Demiris,et al.  Hierarchical attentive multiple models for execution and recognition of actions , 2006, Robotics Auton. Syst..

[11]  Francisco Sandoval Hernández,et al.  Real-time template-based tracking of non-rigid objects using bounded irregular pyramids , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[12]  Katsu Yamane,et al.  Dynamics computation of structure-varying kinematic chains and its application to human figures , 2000, IEEE Trans. Robotics Autom..

[13]  Stefan Schaal,et al.  Is imitation learning the route to humanoid robots? , 1999, Trends in Cognitive Sciences.

[14]  Changming Sun,et al.  Fast Stereo Matching Using Rectangular Subregioning and 3D Maximum-Surface Techniques , 2002, International Journal of Computer Vision.

[15]  Aude Billard,et al.  View Sensitive Cells as a Neural Basis for the Representation of Others in a Self-Centered Frame of Reference , 2005 .

[16]  K. Dautenhahn,et al.  Imitation in Animals and Artifacts , 2002 .

[17]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[18]  Jun Nakanishi,et al.  Movement imitation with nonlinear dynamical systems in humanoid robots , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[19]  Cynthia Breazeal,et al.  Learning From and About Others: Towards Using Imitation to Bootstrap the Social Understanding of Others by Robots , 2005, Artificial Life.