Learning control strategy in soft robotics through a set of configuration spaces
<|reference_start|>Learning control strategy in soft robotics through a set of configuration spaces: The ability of a soft robot to perform specific tasks is determined by its contact configuration, and transitioning between configurations is often necessary to reach a desired position or manipulate an object. Based on this observation, we propose a method for controlling soft robots that involves defining a graph of configuration spaces. Different agents, whether learned or not (convex optimization, expert trajectory, and collision detection), use the structure of the graph to solve the desired task. The graph and the agents are part of the prior knowledge that is intuitively integrated into the learning process. They are used to combine different optimization methods, improve sample efficiency, and provide interpretability. We construct the graph based on the contact configurations and demonstrate its effectiveness through two scenarios, a deformable beam in contact with its environment and a soft manipulator, where it outperforms the baseline in terms of stability, learning speed, and interpretability.<|reference_end|>
@article{ménager2024learning,
    title={Learning control strategy in soft robotics through a set of
  configuration spaces},
    author={Etienne M'enager (DEFROST), Christian Duriez (DEFROST, CRIStAL)},
    journal={IEEE International Conference on Soft Robotics (RoboSoft), IEEE,
  Apr 2024, San Diego (CA), United States},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13649},
    primaryClass={cs.RO}
}