Learning control strategy in soft robotics through a set of configuration spaces
CoRR(2024)
摘要
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.
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关键词
Modelling,Control,Learning for Soft Robots. Soft Robot Applications
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