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
File
Authors
Projects associated
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.
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