Learning to Control a Low-Cost Manipulator Using Data-Efficient Reinforcement Learning
Submitted by mkrainin on Tue, 2012-02-28 11:55
| Title | Learning to Control a Low-Cost Manipulator Using Data-Efficient Reinforcement Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2011 |
| Authors | Deisenroth M, Fox D |
| Conference Name | RSS |
| Abstract | <p>Over the last years, there has been substantial progress in robust manipulation in unstructured environments. The long-term goal of our work is to get away from precise, but very expensive robotic systems and to develop affordable, potentially imprecise, self-adaptive manipulator systems that can interactively perform tasks such as playing with children. In this paper, we demonstrate how a low-cost off-the-shelf robotic system can learn closed-loop policies for a stacking task in only a handful of trials—from scratch. Our manipulator is inaccurate and provides no pose feedback. For learning a controller in the work space of a Kinect-style depth camera, we use a model-based reinforcement learning technique. Our learning method is data efficient, reduces model bias, and deals with several noise sources in a principled way during long-term planning. We present a way of incorporating state-space constraints into the learning process and analyze the learning gain by exploiting the sequential structure of the stacking task.</p> |
| Downloads | |
| Citation Key | Dei11Lea |
Last changed Tue, 2012-03-27 12:36

cs.