Toward Effective Soft Robot Control via Reinforcement Learning

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A soft robot is a kind of robot that is constructed with soft, deformable and elastic materials. Control of soft robots presents complex modeling and planning challenges. We introduce a new approach to accomplish that, making two key contributions: designing an abstract representation of the state of soft robots, and developing a reinforcement learning method to derive effective control policies. The reinforcement learning process can be trained quickly by ignoring the specific materials and structural properties of the soft robot. We apply the approach to the Honeycomb PneuNets Soft Robot and demonstrate the effectiveness of the training method and its ability to produce good control policies under different conditions.

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@inproceedings{ZCZicira17,
 address = {Wuhan, China},
 author = {Haochong Zhang and Rongyun Cao and Shlomo Zilberstein and Feng Wu and Xiaoping Chen},
 booktitle = {Proceedings of the 10th International Conference on Intelligent Robotics and Applications (ICIRA)},
 doi = {10.1007/978-3-319-65289-4_17},
 month = {August},
 pages = {173-184},
 title = {Toward Effective Soft Robot Control via Reinforcement Learning},
 year = {2017}
}