Offline Goal-Conditioned Reinforcement Learning for Shape Control of Deformable Linear Objects
arxiv(2024)
摘要
Deformable objects present several challenges to the field of robotic
manipulation. One of the tasks that best encapsulates the difficulties arising
due to non-rigid behavior is shape control, which requires driving an object to
a desired shape. While shape-servoing methods have been shown successful in
contexts with approximately linear behavior, they can fail in tasks with more
complex dynamics. We investigate an alternative approach, using offline RL to
solve a planar shape control problem of a Deformable Linear Object (DLO). To
evaluate the effect of material properties, two DLOs are tested namely a soft
rope and an elastic cord. We frame this task as a goal-conditioned offline RL
problem, and aim to learn to generalize to unseen goal shapes. Data collection
and augmentation procedures are proposed to limit the amount of experimental
data which needs to be collected with the real robot. We evaluate the amount of
augmentation needed to achieve the best results, and test the effect of
regularization through behavior cloning on the TD3+BC algorithm. Finally, we
show that the proposed approach is able to outperform a shape-servoing baseline
in a curvature inversion experiment.
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