Imitation Learning and Behavior Generation in a Robot Team

In this paper, we propose a method to apply robotic imitation learning in robot teams. In our method, behavior primitives with task-relevant information are defined as the basic units for robots to complete a task. Each behavior has its own task-relevant affordances. The learned behavior primitives format TAEM based behavior libraries and are stored in a database for robots to share. The motivation of learning, conducted by robots, is goal-oriented and strongly related to the given task. Given a task, a robot analyzes the environment and searches the behavior library to find suitable behaviors to generate a behavior sequence to complete the task. If it thinks that it cannot complete this task, this robot requests other robots for assistance or request a human teacher to demonstrate the required behaviors. The newly learned behaviors will be added into the existing behavior library. We also develop inhibiting properties for robots to evaluate the current behaviors, which enables robots to request collaborations from other robots. The experimental results show the validity our proposed method.

[1]  Gunnar Rätsch,et al.  Advanced Lectures on Machine Learning , 2004, Lecture Notes in Computer Science.

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

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

[4]  Sethu Vijayakumar,et al.  Latent spaces for dynamic movement primitives , 2009, 2009 9th IEEE-RAS International Conference on Humanoid Robots.

[5]  Qian Du,et al.  Robots Learn Writing , 2012, J. Robotics.

[6]  Qian Du,et al.  A framework for cognitive robots to learn behaviors through imitation and interaction with humans , 2012, 2012 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support.

[7]  Huan Tan,et al.  Implementation of a Framework for Imitation Learning on a Humanoid Robot Using a Cognitive Architecture , 2012 .

[8]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[9]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[10]  Jun Nakanishi,et al.  Movement imitation with nonlinear dynamical systems in humanoid robots , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[11]  Kazuhiko Kawamura,et al.  A computational framework for integrating robotic exploration and human demonstration in imitation learning , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[12]  Qian Du,et al.  A potential field method-based extension of the dynamic movement primitive algorithm for imitation learning with obstacle avoidance , 2011, 2011 IEEE International Conference on Mechatronics and Automation.

[13]  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).

[14]  Victor R. Lesser.  Evolution of the GPGP/TÆMS domain-independent coordination framework , 2002, AAMAS.

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

[16]  Rüdiger Dillmann,et al.  Learning Robot Behaviour and Skills Based on Human Demonstration and Advice: The Machine Learning Paradigm , 2000 .