Learning tasks from a single demonstration

Learning a complex dynamic robot manoeuvre from a single human demonstration is difficult. This paper explores an approach to learning from demonstration based on learning an optimization criterion from the demonstration and a task model from repeated attempts to perform the task, and using the learned criterion and model to compute an appropriate robot movement. A preliminary version of the approach has been implemented on an anthropomorphic robot arm using a pendulum swing up task as an example.

[1]  M. Ciletti,et al.  The computation and theory of optimal control , 1972 .

[2]  P. Ribeaux,et al.  Learning and Skill Acquisition , 1978 .

[3]  T. Lozano-Perez,et al.  Robot programming , 1983, Proceedings of the IEEE.

[4]  Dean Pomerleau,et al.  Efficient Training of Artificial Neural Networks for Autonomous Navigation , 1991, Neural Computation.

[5]  J. Blackburn,et al.  Stability and Hopf bifurcations in an inverted pendulum , 1992 .

[6]  Mitsuo Kawato,et al.  Teaching by Showing in Kendama Based on Optimization Principle , 1994 .

[7]  Mark W. Spong,et al.  The swing up control problem for the Acrobot , 1995 .

[8]  Yangsheng Xu,et al.  Human skill transfer: neural networks as learners and teachers , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[9]  Stefan Schaal,et al.  From Isolation to Cooperation: An Alternative View of a System of Experts , 1995, NIPS.

[10]  Avinash C. Kak,et al.  Automatic learning of assembly tasks using a DataGlove system , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[11]  Nathan Delson,et al.  Robot programming by human demonstration: adaptation and inconsistency in constrained motion , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[12]  S. Schaal,et al.  A Kendama Learning Robot Based on Bi-directional Theory , 1996, Neural Networks.

[13]  Gregory Z. Grudic,et al.  Human-to-robot skill transfer using the SPORE approximation , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[14]  Peter Bakker,et al.  Robot see, robot do: An overview of robot imitation , 1996 .

[15]  Takashi Suehiro,et al.  Designing Skills with Visual Feedback for APO , 1996 .

[16]  Ales Ude,et al.  Integration of Symbolic and Subsymbolic Learning to Support Robot Programming by Human Demonstration , 1996 .