Construction of a space of motion labels from their mapping to full-body motion symbols

Language is an indispensable for humanoid robot to be integrated into daily life. This paper proposes a novel approach to construct a space of motion labels from their mapping to human whole body motions. The motions are abstracted by Hidden Markov Models, which are referred to as motion symbols. The human motions are automatically partitioned into motion segments, and recognized as sequences of the motion symbols. Sequences of motion labels are also assigned to these motions. The referential relationship between the motion symbols and the motion labels is extracted by stochastic translation model, and distances among the labels are calculated from the association probability of the motion symbols being generated by the labels. The labels are located in a multidimensional space so that the distances are satisfied, and it results in a label space. The label space encapsulates relations among the motion labels such as their similarities. The label space also allows motion recognition. The validity of the constructed label space is demonstrated on a motion capture data-set. Graphical Abstract

[1]  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.

[2]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

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

[4]  C. Breazeal,et al.  Robots that imitate humans , 2002, Trends in Cognitive Sciences.

[5]  Daniel Grest,et al.  Parametric Hidden Markov Models for Recognition and Synthesis of Movements , 2007, BMVC.

[6]  Peter Gärdenfors,et al.  A Framework for Representing Action Meaning in Artificial Systems via Force Dimensions , 2012, AGI.

[7]  Aude Billard,et al.  Discriminative and adaptive imitation in uni-manual and bi-manual tasks , 2006, Robotics Auton. Syst..

[8]  Tetsuya Ogata,et al.  Two-way translation of compound sentences and arm motions by recurrent neural networks , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Brett Browning,et al.  A survey of robot learning from demonstration , 2009, Robotics Auton. Syst..

[10]  Darwin G. Caldwell,et al.  Learning and Reproduction of Gestures by Imitation , 2010, IEEE Robotics & Automation Magazine.

[11]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[12]  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.

[13]  Aude Billard,et al.  On learning the statistical representation of a task and generalizing it to various contexts , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[14]  Yoshihiko Nakamura,et al.  Polynomial design of the nonlinear dynamics for the brain-like information processing of whole body motion , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[15]  Jun Tani,et al.  Learning Semantic Combinatoriality from the Interaction between Linguistic and Behavioral Processes , 2005, Adapt. Behav..

[16]  Dana Kulic,et al.  Representability of human motions by factorial hidden Markov models , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[18]  Naoto Iwahashi,et al.  Motion recognition and generation by combining reference-point-dependent probabilistic models , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Richard A. Harshman,et al.  Indexing by Latent Semantic Analysis , 1990, J. Am. Soc. Inf. Sci..

[20]  Thomas Hofmann,et al.  Probabilistic Latent Semantic Analysis , 1999, UAI.

[21]  Robert L. Mercer,et al.  The Mathematics of Statistical Machine Translation: Parameter Estimation , 1993, CL.

[22]  Yiannis Aloimonos,et al.  A Sensory-Motor Language for Human Activity Understanding , 2006, 2006 6th IEEE-RAS International Conference on Humanoid Robots.

[23]  G. Rizzolatti,et al.  Neurophysiological mechanisms underlying the understanding and imitation of action , 2001, Nature Reviews Neuroscience.

[24]  Tamim Asfour,et al.  Imitation Learning of Dual-Arm Manipulation Tasks in Humanoid Robots , 2006, 2006 6th IEEE-RAS International Conference on Humanoid Robots.

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

[26]  F. Saussure,et al.  Course in General Linguistics , 1960 .

[27]  Yiannis Aloimonos,et al.  A Language for Human Action , 2007, Computer.

[28]  Katsu Yamane,et al.  Capture Database through Symbolization, Recognition and Generation of Motion Patterns , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

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