Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisition

TL;DR

This paper presents a joint human-robot learning system that improves data collection efficiency for robot manipulation by sharing control and gradually automating tasks, validated through simulations and real-world experiments.

Contribution

A novel joint learning system enabling shared control between humans and robots, reducing human effort and improving data collection for manipulation skills.

Findings

Enhanced data collection efficiency demonstrated in experiments.

Reduced human effort needed for robot training.

Maintained data quality suitable for downstream tasks.

Abstract

Employing a teleoperation system for gathering demonstrations offers the potential for more efficient learning of robot manipulation. However, teleoperating a robot arm equipped with a dexterous hand or gripper, via a teleoperation system presents inherent challenges due to the task's high dimensionality, complexity of motion, and differences between physiological structures. In this study, we introduce a novel system for joint learning between human operators and robots, that enables human operators to share control of a robot end-effector with a learned assistive agent, simplifies the data collection process, and facilitates simultaneous human demonstration collection and robot manipulation training. As data accumulates, the assistive agent gradually learns. Consequently, less human effort and attention are required, enhancing the efficiency of the data collection process. It also allows the human operator to adjust the control ratio to achieve a trade-off between manual and automated control. We conducted experiments in both simulated environments and physical real-world settings. Through user studies and quantitative evaluations, it is evident that the proposed system could enhance data collection efficiency and reduce the need for human adaptation while ensuring the collected data is of sufficient quality for downstream tasks. \textit{For more details, please refer to our webpage https://norweig1an.github.io/HAJL.github.io/.