Motion Learning and Adaptive Impedance for Robot Control during Physical Interaction with Humans - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2011

Motion Learning and Adaptive Impedance for Robot Control during Physical Interaction with Humans

Abstract

This article combines programming by demonstration and adaptive control for teaching a robot to physically interact with a human in a collaborative task requiring sharing of a load by the two partners. Learning a task model allows the robot to anticipate the partner's intentions and adapt its motion according to perceived forces. As the human represents a highly complex contact environment, direct reproduction of the learned model may lead to sub-optimal results. To compensate for unmodelled uncertainties, in addition to learning we propose an adaptive control algorithm that tunes the impedance parameters, so as to ensure accurate reproduction. To facilitate the illustration of the concepts introduced in this paper and provide a systematic evaluation, we present experimental results obtained with simulation of a dyad of two planar 2-DOF robots.
No file

Dates and versions

lirmm-00781272 , version 1 (25-01-2013)

Identifiers

  • HAL Id : lirmm-00781272 , version 1

Cite

Elena Gribovskaya, Abderrahmane Kheddar, Aude Billard. Motion Learning and Adaptive Impedance for Robot Control during Physical Interaction with Humans. ICRA: International Conference on Robotics and Automation, May 2011, Shanghai, China. pp.4326-4332. ⟨lirmm-00781272⟩
627 View
0 Download

Share

Gmail Mastodon Facebook X LinkedIn More