Shape Control of Elastoplastic Deformable Linear Objects through Reinforcement Learning
Övrigt konferensbidrag, 2020

Deformable object manipulation tasks have longbeen regarded as challenging robotic problems. However, untilrecently, very little work had been done on the subject, withmost robotic manipulation methods being developed for rigidobjects. As machine learning methods are becoming morepowerful, there are new model-free strategies to explore forthese objects, which are notoriously hard to model. This paperfocuses on shape control problems for Deformable Linear Objects (DLOs). Despite being one of the most researched classesof DLOs in terms of geometry, no other paper has focusedon materials with elastoplastic properties. Therefore, a novelshape control task, requiring permanent plastic deformationis implemented in a simulation environment. ReinforcementLearning methods are used to learn a continuous controlpolicy. To that end, a discrete curvature measure is usedas a low-dimensional state representation and as part of anintuitive reward function. Finally, three state-of-the-art actor-critic algorithms are compared on the proposed environmentand successfully achieve the goal shape.

Reinforcement Learning


Deformable Object Manipulation


Rita Laezza

Chalmers, Elektroteknik, System- och reglerteknik

Yiannis Karayiannidis

Chalmers, Elektroteknik, System- och reglerteknik

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Las Vegas (Virtual), USA,


C3SE (Chalmers Centre for Computational Science and Engineering)


Robotteknik och automation


Datorseende och robotik (autonoma system)

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