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Journal Articles IEEE Robotics and Automation Letters Year : 2021

Bilateral teleoperation with adaptive impedance control for contact tasks


This paper presents an adaptive impedance control architecture for robotic teleoperation of contact tasks featuring continuous interaction with the environment. We use Learning from Demonstration (LfD) 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 variablestiffness approach outperforms two standard constant-stiffness approaches in terms of safety and robot tracking performance.
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hal-03161641 , version 1 (07-03-2021)



Youssef Michel, Rahaf Rahal, Claudio Pacchierotti, Paolo Robuffo Giordano, Dongheui Lee. Bilateral teleoperation with adaptive impedance control for contact tasks. IEEE Robotics and Automation Letters, 2021, 6 (3), pp.5429-5436. ⟨10.1109/LRA.2021.3066974⟩. ⟨hal-03161641⟩
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