

Publication Details
Reference Type  Conference Proceedings 
Author(s)  Peters, J.;Schaal, S. 
Year  2006 
Title  Learning operational space control 
Journal/Conference/Book Title  Robotics: Science and Systems (RSS 2006) 
Keywords  operational space control
redundancy
forward models
inverse models
compliance
reinforcement leanring
locally weighted learning 
Abstract  While operational space control is of essential importance for robotics and wellunderstood from an analytical point of view, it can be prohibitively hard to achieve accurate control in face of modeling errors, which are inevitable in complex robots, e.g., humanoid robots. In such cases, learning control methods can offer an interesting alternative to analytical control algorithms. However, the resulting learning problem is illdefined as it requires to learn an inverse mapping of a usually redundant system, which is well known to suffer from the property of noncovexity of the solution space, i.e., the learning system could generate motor commands that try to steer the robot into physically impossible configurations. A first important insight for this paper is that, nevertheless, a physically correct solution to the inverse problem does exits when learning of the inverse map is performed in a suitable piecewise linear way. The second crucial component for our work is based on a recent insight that many operational space controllers can be understood in terms of a constraint optimal control problem. The cost function associated with this optimal control problem allows us to formulate a learning algorithm that automatically synthesizes a globally consistent desired resolution of redundancy while learning the operational space controller. From the view of machine learning, the learning problem corresponds to a reinforcement learning problem that maximizes an immediate reward and that employs an expectationmaximization policy search algorithm. Evaluations on a three degrees of freedom robot arm illustrate the feasability of our suggested approach.

Editor(s)  Burgard, W.;Sukhatme, G. S.;Schaal, S. 
Place Published  Philadelphia, PA, Aug.1619 
Publisher  Cambridge, MA: MIT Press 
Short Title  Learning operational space control 
URL(s)  http://wwwclmc.usc.edu/publications/P/petersRSS2006.pdf


