Gribovskaya, Elena (EPFL), Kheddar, Abderrahmane (CNRS), Billard, Aude (EPFL)

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

Scheduled for presentation during the Regular Sessions "Learning and Adaptive Systems II" (WeP212), Wednesday, May 11, 2011, 16:10−16:25, Room 5H

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on December 8, 2019

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.