Learning responsive robot behavior by imitation

In this paper we present a new approach for learning responsive robot behavior by imitation of human interaction partners. Extending previous work on robot imitation learning, that has so far mostly concentrated on learning from demonstrations by a single actor, we simultaneously record the movements of two humans engaged in on-going interaction tasks and learn compact models of the interaction. Extracted interaction models can thereafter be used by a robot to engage in a similar interaction with a human partner. We present two algorithms for deriving interaction models from motion capture data as well as experimental results on a humanoid robot.

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

[2]  Yoshihiko Nakamura,et al.  Mimetic Communication Model with Compliant Physical Contact in Human—Humanoid Interaction , 2010, Int. J. Robotics Res..

[3]  Stefan Schaal,et al.  Robot Programming by Demonstration , 2009, Springer Handbook of Robotics.

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

[5]  Michael A Riley,et al.  Synergies in intra- and interpersonal interlimb rhythmic coordination. , 2007, Motor control.

[6]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[7]  Ben J. A. Kröse,et al.  Active Appearance-Based Robot Localization Using Stereo Vision , 2005, Auton. Robots.

[8]  Le Song,et al.  Hilbert Space Embeddings of Hidden Markov Models , 2010, ICML.

[9]  Aaron Hertzmann,et al.  Style machines , 2000, SIGGRAPH 2000.

[10]  Joris De Schutter,et al.  Constraint-based Task Specification and Estimation for Sensor-Based Robot Systems in the Presence of Geometric Uncertainty , 2007, Int. J. Robotics Res..

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

[12]  Darwin G. Caldwell,et al.  Evaluation of a probabilistic approach to learn and reproduce gestures by imitation , 2010, 2010 IEEE International Conference on Robotics and Automation.

[13]  Jun Tani,et al.  On-line Imitative Interaction with a Humanoid Robot Using a Dynamic Neural Network Model of a Mirror System , 2004, Adapt. Behav..

[14]  Emilio Bizzi,et al.  Combinations of muscle synergies in the construction of a natural motor behavior , 2003, Nature Neuroscience.

[15]  Pieter Abbeel,et al.  Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion , 2007, NIPS.

[16]  Charles Bouveyron,et al.  Intrinsic dimension estimation by maximum likelihood in isotropic probabilistic PCA , 2011, Pattern Recognit. Lett..

[17]  Bernhard Schölkopf,et al.  Probabilistic Modeling of Human Dynamics for Intention Inference , 2012, Robotics: Science and Systems Conference.

[18]  J. F. Soechting,et al.  Postural Hand Synergies for Tool Use , 1998, The Journal of Neuroscience.

[19]  Edmond S. L. Ho,et al.  Spatial relationship preserving character motion adaptation , 2010, ACM Trans. Graph..

[20]  Jun Nakanishi,et al.  Learning Attractor Landscapes for Learning Motor Primitives , 2002, NIPS.

[21]  Takashi Minato,et al.  Physical Human-Robot Interaction: Mutual Learning and Adaptation , 2012, IEEE Robotics & Automation Magazine.

[22]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[23]  David Vogt,et al.  Inferring guidance information in cooperative human-robot tasks , 2013, 2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids).