Force, impedance and trajectory learning for contact tooling and haptic identification

Li, Yanan, Ganesh, Gowrishankar, Jarrassé, Nathanaël, Haddadin, Sami, Albu-Schaeffer, Alin and Burdet, Etienne (2018) Force, impedance and trajectory learning for contact tooling and haptic identification. IEEE Transactions on Robotics, 34 (5). pp. 1170-1182. ISSN 1552-3098

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Humans can skilfully use tools and interact with the environment by adapting their movement trajectory, contact force, and impedance. Motivated by the human versatility, we develop here a robot controller that concurrently adapts feedforward force, impedance, and reference trajectory when interacting with an unknown environment. In particular, the robot's reference trajectory is adapted to limit the interaction force and maintain it at a desired level, while feedforward force and impedance adaptation compensates for the interaction with the environment. An analysis of the interaction dynamics using Lyapunov theory yields the conditions for convergence of the closed-loop interaction mediated by this controller. Simulations exhibit adaptive properties similar to human motor adaptation. The implementation of this controller for typical interaction tasks including drilling, cutting, and haptic exploration shows that this controller can outperform conventional controllers in contact tooling.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Depositing User: Yanan Li
Date Deposited: 05 Apr 2018 13:57
Last Modified: 02 Jul 2019 13:47

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