Incremental learning of gestures by imitation in a humanoid robot

We present an approach to teach incrementally human gestures to a humanoid robot. By using active teaching methods that puts the human teacher “in the loop” of the robot's learning, we show that the essential characteristics of a gesture can be efficiently transferred by interacting socially with the robot. In a first phase, the robot observes the user demonstrating the skill while wearing motion sensors. The motion of his/her two arms and head are recorded by the robot, projected in a latent space of motion and encoded probabilistically in a Gaussian Mixture Model (GMM). In a second phase, the user helps the robot refine its gesture by kinesthetic teaching, i.e. by grabbing and moving its arms throughout the movement to provide the appropriate scaffolds. To update the model of the gesture, we compare the performance of two incremental training procedures against a batch training procedure. We present experiments to show that different modalities can be combined efficiently to teach incrementally basketball officials' signals to a HOAP-3 humanoid robot.

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

[2]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

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

[4]  David A. Cohn,et al.  Active Learning with Statistical Models , 1996, NIPS.

[5]  Masayuki Inaba,et al.  Learning by watching: extracting reusable task knowledge from visual observation of human performance , 1994, IEEE Trans. Robotics Autom..

[6]  Stefan Schaal,et al.  Robot Learning From Demonstration , 1997, ICML.

[7]  Ran,et al.  The correspondence problem , 1998 .

[8]  R. Byrne.  Imitation without intentionality. Using string parsing to copy the organization of behaviour , 1999, Animal Cognition.

[9]  Aude Billard,et al.  DRAMA, a Connectionist Architecture for Control and Learning in Autonomous Robots , 1999, Adapt. Behav..

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

[11]  C. Heyes,et al.  What Is the Significance of Imitation in Animals , 2000 .

[12]  Kerstin Dautenhahn,et al.  Of hummingbirds and helicopters: An algebraic framework for interdisciplinary studies of imitation a , 2000 .

[13]  Ralph Gross,et al.  The CMU Motion of Body (MoBo) Database , 2001 .

[14]  C. Heyes.  Causes and consequences of imitation , 2001, Trends in Cognitive Sciences.

[15]  Chrystopher L. Nehaniv,et al.  Like Me?- Measures of Correspondence and Imitation , 2001, Cybern. Syst..

[16]  C. Breazeal,et al.  Challenges in building robots that imitate people , 2002 .

[17]  Gillian M. Hayes,et al.  Imitation as a dual-route process featuring prediction and learning components: A biologically plaus , 2002 .

[18]  Yoshihiko Nakamura,et al.  Acquiring Motion Elements for Bidirectional Computation of Motion Recognition and Generation , 2002, ISER.

[19]  Chrystopher L. Nehaniv,et al.  Imitation with ALICE: learning to imitate corresponding actions across dissimilar embodiments , 2002, IEEE Trans. Syst. Man Cybern. Part A.

[20]  Stefan Schaal,et al.  http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained , 2007 .

[21]  W. Wong,et al.  On ψ-Learning , 2003 .

[22]  Rüdiger Dillmann,et al.  Teaching and learning of robot tasks via observation of human performance , 2004, Robotics Auton. Syst..

[23]  Aude Billard,et al.  Stochastic gesture production and recognition model for a humanoid robot , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[24]  Samy Bengio,et al.  Multimodal speech processing using asynchronous Hidden Markov Models , 2004, Inf. Fusion.

[25]  Gordon Cheng,et al.  Discovering optimal imitation strategies , 2004, Robotics Auton. Syst..

[26]  Robert R. Horn,et al.  Observational learning : Is it time we took another look? , 2004 .

[27]  Rajesh P. N. Rao,et al.  Robotic imitation from human motion capture using Gaussian processes , 2005, 5th IEEE-RAS International Conference on Humanoid Robots, 2005..

[28]  Hongbin Wang,et al.  Highly efficient incremental estimation of Gaussian mixture models for online data stream clustering , 2005, SPIE Defense + Commercial Sensing.

[29]  Stan Sclaroff,et al.  Handsignals Recognition From Video Using 3D Motion Capture Data , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[30]  Roberto Cipolla,et al.  Incremental Learning of Temporally-Coherent Gaussian Mixture Models , 2005, BMVC.

[31]  Rüdiger Dillmann,et al.  Towards Cognitive Robots: Building Hierarchical Task Representations of Manipulations from Human Demonstration , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[32]  Stefan Schaal,et al.  Incremental Online Learning in High Dimensions , 2005, Neural Computation.

[33]  Aude Billard,et al.  Goal-Directed Imitation in a Humanoid Robot , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[34]  Chrystopher L. Nehaniv,et al.  An Approach for Programming Robots by Demonstration: Generalization Across Different Initial Configurations of Manipulated Objects , 2005, 2005 International Symposium on Computational Intelligence in Robotics and Automation.

[35]  Aude Billard,et al.  Teaching a Humanoid Robot to Recognize and Reproduce Social Cues , 2006, ROMAN 2006 - The 15th IEEE International Symposium on Robot and Human Interactive Communication.

[36]  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..

[37]  Chrystopher L. Nehaniv,et al.  Teaching robots by moulding behavior and scaffolding the environment , 2006, HRI '06.

[38]  Jun Tani,et al.  Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model , 2006, Neural Networks.

[39]  Masayuki Inaba,et al.  Situation Recognition and Behavior Induction based on Geometric Symbol Representation of Multimodal Sensorimotor Patterns , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[40]  Aude Billard,et al.  On Learning, Representing, and Generalizing a Task in a Humanoid Robot , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[41]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.