Research Area:  Machine Learning

Abstract:

Robotic assembly tasks require precise manipulation and coordination, often necessitating advanced learning techniques to achieve efficient and effective performance. While residual reinforcement learning with a base policy has shown promise in this domain, existing base policy approaches often rely on hand-designed full-state features and policies or extensive demonstrations, limiting their applicability in semi-structured environments.In this study, we propose an innovative Object-Embodiment-Centric Imitation and Residual Reinforcement Learning (OEC-IRRL) approach that leverages an object-embodiment-centric (OEC) task representation to integrate vision models with imitation and residual learning. By utilizing a single demonstration and minimizing interactions with the environment, our method aims to enhance learning efficiency and effectiveness. The proposed method involves three key steps: creating an object-embodiment-centric task representation, employing imitation learning for a base policy using via-point movement primitives for generalization to different settings, and utilizing residual RL for uncertainty-aware policy refinement during the assembly phase.This research presents a promising avenue for robotic assembly tasks, providing a viable solution without the need for specialized expertise or custom fixtures.

Keywords:  

Author(s) Name:  Chuang Wang, Chupeng Su, Baozheng Sun, Gang Chen, Longhan Xie

Publisher name:  Frontiers

DOI:  10.3389/fnbot.2024.1355170

Volume Information:  Volume 18, (2024)

Paper Link:   https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2024.1355170/full