Bootstrapping Robotic Skill Learning With Intuitive Teleoperation: Initial Feasibility Study

Robotic skill learning has been increasingly studied but the demonstration collections are more challenging compared to collecting images/videos in computer vision and texts in natural language processing. This paper presents a skill learning paradigm by using intuitive teleoperation devices to gene...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Chu, Xiangyu, Tang, Yunxi, Kwok, Lam Him, Cai, Yuanpei, Kwok Wai Samuel Au
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 11.11.2023
Subjects
Computer vision
Data collection
Feasibility studies
Natural language processing
Robot learning
Online AccessGet full text

Cover

More Information
Summary:Robotic skill learning has been increasingly studied but the demonstration collections are more challenging compared to collecting images/videos in computer vision and texts in natural language processing. This paper presents a skill learning paradigm by using intuitive teleoperation devices to generate high-quality human demonstrations efficiently for robotic skill learning in a data-driven manner. By using a reliable teleoperation interface, the da Vinci Research Kit (dVRK) master, a system called dVRK-Simulator-for-Demonstration (dS4D) is proposed in this paper. Various manipulation tasks show the system's effectiveness and advantages in efficiency compared to other interfaces. Using the collected data for policy learning has been investigated, which verifies the initial feasibility. We believe the proposed paradigm can facilitate robot learning driven by high-quality demonstrations and efficiency while generating them.
ISSN:2331-8422