Discovering optimal imitation strategies

• DOI: 10.1016/j.robot.2004.03.002
• Publication Date: 2004-05-29T02:54:22Z
• Authors: Aude Billard; Yann Epars; Sylvain Calinon; Stefan Schaal; Gordon Cheng
• Keywords: Imitation Learning; 0209 industrial biotechnology; Bayesian Learning; Programming by Demonstration; 02 engineering and technology; Humanoid Robots; Learning by imitation - Robotics; Artificial Neural Networks; Hidden Markov Models

This paper develops a general policy for learning the relevant features of an imitation task. We restrict our study to imitation of manipulative tasks or gestures. The imitation process is modeled as a hierarchical optimization system, which minimizes the discrepancy between two multi-dimensional datasets. To classify across manipulation strategies, we apply a probabilistic analysis to data in Cartesian and joint spaces. We determine a general metric that optimizes the policy of task reproduction, following strategy determination. The model successfully discovers strategies in six different imitative tasks and controls task reproduction by a full body humanoid robot. © 2004 Elsevier B.V. All rights reserved.