Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisition

1Shanghai Jiao Tong University, 2University of Illinois, Urbana Champaign

Traditional frameworks typically separate human and agent training, requiring operators first to learn the task environment before data collection. In our framework, we integrate human and agent training from the start in a joint learning model, enables simultaneous development and adapts the agents to human operation more effectively, enhancing overall efficiency and promoting better collaboration between humans and machines allowing for human effortless adaptation data collection.


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 poses significant challenges due to its high dimensionality, complex motions, and differences in physiological structure.

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, facilitating simultaneous human demonstration collection and robot manipulation teaching. In this setup, 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.