Robot Learning for Deformable Object Manipulation Tasks
Doktorsavhandling, 2024
This thesis tackles DOM problems using ML techniques for tasks with Deformable Linear Objects (DLOs), e.g. ropes and cables, found in a variety of industrial applications. DLOs encapsulate a lot of the general challenges in DOM, making them good case studies on the effectiveness of ML for other types of deformable objects. Typically, ML algorithms require large amounts of data that are better satisfied in simulation. Therefore, the ReForm simulation sandbox is introduced, which includes six DLO manipulation tasks. ReForm aims to facilitate comparison and reproducibility of robot learning research on tasks where the goal is to control the shape of a DLO. Such shape control tasks are categorized as: explicit, if a precise shape is to be achieved; or implicit, if its deformation is dictated by a more abstract goal.
Two representative DLO manipulation tasks are addressed: (i) shape-servoing (explicit) and (ii) cable-routing (implicit). For shape-servoing, special emphasis is given to Reinforcement Learning (RL) methods. Initial work tackles shape-servoing of an elastoplastic DLO towards a unique goal, using online RL with ReForm. Subsequent work moves towards a multi-goal task in a real-world experimental setup, using offline RL methods to learn directly from real data. In the cable-routing works, the aim is to lay the groundwork for solving this type of task through motion primitives, with limited use of ML. First, a vision-based approach is presented, which is able to route a cable through randomly placed fixtures. Then, a force-based approach is introduced for a similar problem, in which the state and stiffness of a DLO can be estimated through contact with fixtures.
Deformable Object Manipulation
Deformable Linear Objects
Robotics
Reinforcement Learning
Machine Learning
Robot Learning
Författare
Rita Laezza
Chalmers, Elektroteknik, System- och reglerteknik
ReForm: A Robot Learning Sandbox for Deformable Linear Object Manipulation
Proceedings - IEEE International Conference on Robotics and Automation,;Vol. 2021-May(2021)p. 4717-4723
Paper i proceeding
Learning Shape Control of Elastoplastic Deformable Linear Objects
Proceedings - IEEE International Conference on Robotics and Automation,;Vol. 2021-May(2021)p. 4438-4444
Paper i proceeding
Offline Goal-Conditioned Reinforcement Learning for Shape Control of Deformable Linear Objects
Preprint
Planning and Control for Cable-routing with Dual-arm Robot
Proceedings - IEEE International Conference on Robotics and Automation,;Vol. 2022-May(2022)p. 1046-1052
Paper i proceeding
Feel the Tension: Manipulation of Deformable Linear Objects in Environments with Fixtures using Force Information
IEEE International Conference on Intelligent Robots and Systems,;Vol. 2022-October(2022)p. 11216-11222
Paper i proceeding
Infrastruktur
C3SE (Chalmers Centre for Computational Science and Engineering)
Ämneskategorier
Robotteknik och automation
ISBN
978-91-8103-030-3
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5488
Utgivare
Chalmers