Bio-inspired robotic impedance adaptation for human-robot collaborative tasks

Abstract

To improve the robotic flexibility and dexterity in a human-robot collaboration task, it is important to adapt the robot impedance in a real-time manner to its partners behavior. However, it is often quite challenging to achieve this goal and has not been well addressed yet. In this paper, we propose a bio-inspired approach as a possible solution, which enables the online adaptation of robotic impedance in the unknown and dynamic environment. Specifically, the bio-inspired mechanism is derived from the human motor learning, and it can automatically adapt the robotic impedance and feedforward torque along the motion trajectory. It can enable the learning of compliant robotic behaviors to meet the dynamic requirements of the interactions. In order to validate the proposed approach, an experiment containing an anti-disturbance test and a human-robot collaborative sawing task has been conducted.