Empowering Robot Teleoperation: Exploring the Synergies Between Devices and Manipulator Controllers in a Comparative Study

TL;DR

This study investigates how different teleoperation devices and control strategies affect robot manipulation performance, emphasizing the importance of device-controller synergy for effective robot learning and teleoperation.

Contribution

It provides a comparative analysis of device-controller pairings in robot teleoperation, highlighting their impact on data collection and manipulation task effectiveness.

Findings

Device-controller pairing significantly influences manipulation success.

Certain device and control combinations yield better data quality for learning.

Analysis underscores the importance of matching teleoperation tools with appropriate controllers.

Abstract

Robot learning empowers the robot system with human brain-like intelligence to autonomously acquire and adapt skills through experience, enhancing flexibility and adaptability in various environments. Aimed at achieving a similar level of capability in large language models (LLMs) for embodied intelligence, data quality plays a crucial role in training a foundational model with diverse robot skills. In this study, we investigate the collection of data for manipulation tasks using teleoperation devices. Different devices yield varying effects when paired with corresponding controller strategies, including position-based inverse kinematic (IK) control, torque-based inverse dynamic (ID) control, and optimization-based compliant control. Analysis of experimental results suggests the importance of the relationship between teleoperation devices and controllers for real tasks.