Offline Goal-Conditioned Reinforcement Learning for Shape Control of Deformable Linear Objects
Preprint, 2024

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.

Författare

Rita Laezza

Chalmers, Elektroteknik, System- och reglerteknik

Mohammadreza Shetab-Bushehri

Université Clermont Auvergne

Gabriel Arslan Waltersson

Chalmers, Elektroteknik, System- och reglerteknik

Erol Özgür

Université Clermont Auvergne

Youcef Mezouar

Université Clermont Auvergne

Yiannis Karayiannidis

Chalmers, Elektroteknik, System- och reglerteknik

Infrastruktur

C3SE (Chalmers Centre for Computational Science and Engineering)

Ämneskategorier

Robotteknik och automation

Datavetenskap (datalogi)

Datorseende och robotik (autonoma system)

Mer information

Senast uppdaterat

2024-03-18