Robust Manipulation Primitive Learning via Domain Contraction

Teng Xue, Amirreza Razmjoo, Suhan Shetty, Sylvain Calinon

Keywords: Robust policy learning, Contact-rich manipulation, Sim-to-real

TL;DR: We propose a bi-level approach to learn parameter-conditioned manipulation policies using multiple models and domain contraction.

Abstract: Contact-rich manipulation plays an important role in everyday life, but uncertain parameters pose significant challenges to model-based planning and control. To address this issue, domain adaptation and domain randomization have been proposed to learn robust policies. However, they either lose the generalization ability to diverse instances or perform conservatively due to neglecting instance-specific information. In this paper, we propose a bi-level approach to learn robust manipulation primitives, including parameter-augmented policy learning using multiple models with tensor approximation, and parameter-conditioned policy retrieval through domain contraction. This approach unifies domain randomization and domain adaptation, providing optimal behaviors while keeping generalization ability. We validate the proposed method on three contact-rich manipulation primitives: hitting, pushing, and reorientation. The experimental results showcase the superior performance of our approach in generating robust policies for instances with diverse physical parameters.

Website: https://sites.google.com/view/robustpl

Student Paper: yes

Submission Number: 356