Purpose
This paper aims to propose a general framework of shared control for human–robot interaction.
Design/methodology/approach
Human dynamics are considered in analysis of the coupled human–robot system. Motion intentions of both human and robot are taken into account in the control objective of the robot. Reinforcement learning is developed to achieve the control objective subject to unknown dynamics of human and robot. The closed-loop system performance is discussed through a rigorous proof.
Findings
Simulations are conducted to demonstrate the learning capability of the proposed method and its feasibility in handling various situations.
Originality/value
Compared to existing works, the proposed framework combines motion intentions of both human and robot in a human–robot shared control system, without the requirement of the knowledge of human’s and robot’s dynamics.
DEEP REINFORCEMENT LEARNING FOR HAPTIC SHARED CONTROL IN UNKNOWN TASKS