Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning

@inproceedings{Deisenroth2011LearningTC,
  title={Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning},
  author={Marc Peter Deisenroth and Carl Edward Rasmussen and Dieter Fox},
  booktitle={Robotics: Science and Systems},
  year={2011}
}
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… 

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