Theory of Communication between Human and Humanoid Robot based on Embodied Symbol Model

This paper describes a novel approach to modeling behavioral communication for humanoid robots that interact with their partners. Communication is established based on reaction through recognition of motion patterns of partners. Previous work of symbolization of motion primitives using Hidden Markov Models (HMMs) allows robots to recognize the observation and generate their own behaviors separately. In this paper we proposes a hierarchical model for communication, where HMMs in a lower layer abstract motion primitives of a robot and its partner and HMMs in a upper layer abstract interaction patterns. In the upper layer, output is recognition result of current interaction and input is generation of interaction. Shortcut between the output and input maintains the current interaction and realizes behavioral communication between the robot and the partner. Experiments of a humanoid robot interacting with its partner in a virtual world validate our principle of fundamental communication.

[1]  Takashi Matsuyama,et al.  Modeling Timing Structure in Multimedia Signals , 2006, AMDO.

[2]  Kevin P. Murphy,et al.  Linear-time inference in Hierarchical HMMs , 2001, NIPS.

[3]  Yoshihiko Nakamura,et al.  Embodied Symbol Emergence Based on Mimesis Theory , 2004, Int. J. Robotics Res..

[4]  Forrest W. Young,et al.  Nonmetric individual differences multidimensional scaling: An alternating least squares method with optimal scaling features , 1977 .

[5]  Cynthia Breazeal,et al.  A Motivational System for Regulating Human-Robot Interaction , 1998, AAAI/IAAI.

[6]  Eric Horvitz,et al.  Layered representations for learning and inferring office activity from multiple sensory channels , 2004, Comput. Vis. Image Underst..

[7]  Katsu Yamane,et al.  Online dynamical retouch of motion patterns towards animatronic humanoid robots , 2005, 5th IEEE-RAS International Conference on Humanoid Robots, 2005..

[8]  Yoram Singer,et al.  The Hierarchical Hidden Markov Model: Analysis and Applications , 1998, Machine Learning.

[9]  Alex Pentland,et al.  Coupled hidden Markov models for complex action recognition , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Alex Pentland,et al.  A Bayesian Computer Vision System for Modeling Human Interactions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Jun Tani,et al.  Self-organization of behavioral primitives as multiple attractor dynamics: A robot experiment , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[12]  Sridhar Mahadevan,et al.  Learning the hierarchical structure of spatial environments using multiresolution statistical models , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[14]  Roland Siegwart,et al.  Robot learning from demonstration , 2004, Robotics Auton. Syst..

[15]  Stephen E. Levinson,et al.  Continuously variable duration hidden Markov models for automatic speech recognition , 1986 .

[16]  Svetha Venkatesh,et al.  Recognition of human activity through hierarchical stochastic learning , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[17]  Yoshihiko Nakamura,et al.  Hardware design of high performance miniature anthropomorphic robots , 2008, Robotics Auton. Syst..

[18]  Mitsuo Kawato,et al.  MOSAIC Model for Sensorimotor Learning and Control , 2001, Neural Computation.

[19]  Y. Nakamura,et al.  Symbolic memory for humanoid robots using hierarchical bifurcations of attractors in nonmonotonic neural networks , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Svetha Venkatesh,et al.  Learning Hierarchical Hidden Markov Models with General State Hierarchy , 2004, AAAI.

[21]  Yoshihiko Nakamura,et al.  Humanoid Robot's Autonomous Acquisition of Proto-Symbols through Motion Segmentation , 2006, 2006 6th IEEE-RAS International Conference on Humanoid Robots.