Publication Details

SELECT * FROM publications WHERE Record_Number=10512
Reference TypeConference Proceedings
Author(s)Deisenroth, M.P.; Fox, D.; Rasmussen, C.E.
TitleLearning to Control a Low-Cost Robotic Manipulator Using Data-Efficient Reinforcement Learning
Journal/Conference/Book TitleRobotics: Science & Systems (RSS 2011)
KeywordsGaussian process, reinforcement learning, robot learning, policy search
AbstractOver 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 trialsfrom 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.
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