Adaptive Arbitration for Minimal Intervention Shared Control via Deep Reinforcement Learning

In shared control, humans and intelligent robots jointly complete real-time control tasks with their complementary capabilities for improved performance unavailable by neither side on its own, which is attracting more and more attentions in recent years. Arbitration, as an indispensable part of shared control, determines how control authority is allocated between the human and robot, and the definition of that policy has always been one of the fundamental problems. In this paper, we propose an adaptive arbitration method for shared control systems, which minimizes the deviation from the human inputs while ensuring the system performance based on deep reinforcement learning. We provide humans the maximum assistance with the minimal intervention, in order to balance human’s need for control authority and need for performance. We apply our method to real-time control tasks, and the results show that our method achieves high task success rate and shorter task completion time with less human workload, while maintaining higher human satisfaction.