Bilateral Teleoperation With Adaptive Impedance Control for Contact Tasks

This letter presents an adaptive impedance control architecture for robotic teleoperation of contact tasks featuring continuous interaction with the environment. We use Learning from Demonstration (LID) as a framework to learn variable stiffness control policies. Then, the learnt state-varying stiffness is used to command the remote manipulator, so as to adapt its interaction with the environment based on the sensed forces. Our system only relies on the on-board torque sensors of a commercial robotic manipulator and it does not require any additional hardware or user input for the estimation of the required stiffness. We also provide a passivity analysis of our system, where the concept of energy tanks is used to guarantee a stable behavior. Finally, the system is evaluated in a representative teleoperated cutting application. Results show that the proposed variable-stiffness approach outperforms two standard constant-stiffness approaches in terms of safety and robot tracking performance.

Automated summary

The letter introduces an adaptive impedance control architecture for robotic teleoperation of contact tasks, using Learning from Demonstration to learn variable stiffness control policies. The system relies solely on the on-board torque sensors of a commercial robotic manipulator and does not require additional hardware or user input. Evaluation results show that the proposed variable-stiffness approach outperforms standard constant-stiffness approaches in terms of safety and robot tracking performance.