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Extended residual learning with one-shot imitation learning for robotic assembly in semi-structured environment.
Wang, Chuang; Su, Chupeng; Sun, Baozheng; Chen, Gang; Xie, Longhan.
Afiliación
  • Wang C; Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China.
  • Su C; Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China.
  • Sun B; Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China.
  • Chen G; Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China.
  • Xie L; Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China.
Front Neurorobot ; 18: 1355170, 2024.
Article en En | MEDLINE | ID: mdl-38741932
ABSTRACT

Introduction:

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.

Methods:

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.

Results:

Through a series of comprehensive experiments, we investigate the impact of the OEC task representation on base and residual policy learning and demonstrate the effectiveness of the method in semi-structured environments. Our results indicate that the approach, requiring only a single demonstration and less than 1.2 h of interaction, improves success rates by 46% and reduces assembly time by 25%.

Discussion:

This research presents a promising avenue for robotic assembly tasks, providing a viable solution without the need for specialized expertise or custom fixtures.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Neurorobot Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Neurorobot Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza