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- W1520597402 abstract "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." @default.
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- W1520597402 date "2011-06-27" @default.
- W1520597402 modified "2023-10-18" @default.
- W1520597402 title "Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning" @default.
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- W1520597402 doi "https://doi.org/10.15607/rss.2011.vii.008" @default.
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