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Learning compliant assembly motions from demonstration

Markku Suomalainen, Ville Kyrki

Year
2016
Citations
18

Abstract

Automating assembly processes outside controlled factory environments is still rare, mostly because of the inherent position uncertainties. The use of compliant motions allows robustness against the uncertainty, but automatic planning of compliant motion sequences is not computationally feasible. In this paper, we show how compliant assembly motions can be learned from human demonstrations. A human teacher will kinesthetically demonstrate compliant motions where the physical shapes of assembled parts guide the motion. From these demonstrations, the proposed method identifies desired direction of movement, the number of compliant axes and their directions. We use this information to construct an impedance controller which can reproduce the assembly motion despite uncertainty in the starting position. The method is studied with a KUKA LWR4+ arm in two test setups with different number of physically constrained degrees of freedom. The experimental study shows that the method is able to correctly identify the motion parameters and allows the robot to successfully perform the demonstrated assembly motion from various unseen starting positions.

Keywords

Robustness (evolution)Computer scienceMotion (physics)RobotPosition (finance)Degrees of freedom (physics and chemistry)Factory (object-oriented programming)Motion planningArtificial intelligenceMotion control

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