Robotics: Science and Systems VII
Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning
Marc Deisenroth, Carl Rasmussen, Dieter FoxAbstract:
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.
Bibtex:
@INPROCEEDINGS{Deisenroth-RSS-11, AUTHOR = {Marc Deisenroth AND Carl Rasmussen AND Dieter Fox}, TITLE = {Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning}, BOOKTITLE = {Proceedings of Robotics: Science and Systems}, YEAR = {2011}, ADDRESS = {Los Angeles, CA, USA}, MONTH = {June}, DOI = {10.15607/RSS.2011.VII.008} }