Publication

Sharpening haptic inputs for teaching a manipulation skill to a robot

Conference Article

Conference

IEEE International Conference on Applied Bionics and Biomechanics (ICABB)

Edition

1st

Pages

331-340

Doc link

http://www.icabb-iss.org/

File

Download the digital copy of the doc pdf document

Abstract

Gaussian mixtures-based learning algorithms are suitable strategies for trajectory learning and skill acquisition, in the context of programming by demonstration (PbD). Input streams other than visual information, as used in most applications up to date, reveal themselves as quite useful in trajectory learning experiments where visual sources are not available. In this work we have used force/torque feedback through a haptic device for teaching a teleoperated robot to empty a rigid container. Structure vibrations and container inertia appeared to considerably disrupt the sensing process, so a filtering algorithm had to be devised. Moreover, some input variables seemed much more relevant to the particular task to be learned than others, which lead us to analyze the training data in order to select those relevant features through principal component analysis and a mutual information criterion. Then, a batch version of GMM/GMR was implemented using different training datasets (original, pre-processed data through PCA and MI). Tests where the teacher was instructed to follow a strategy compared to others where he was not lead to useful conclusions that permit devising the new research stages.

Categories

intelligent robots, robot programming, telerobotics.

Author keywords

robot learning, GMM, GMR, mutual information

Scientific reference

L. Rozo, P. Jiménez and C. Torras. Sharpening haptic inputs for teaching a manipulation skill to a robot, 1st IEEE International Conference on Applied Bionics and Biomechanics, 2010, Venice, pp. 331-340.