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Learning Robot Manipulation Tasks with Task-Parameterized Semi-Tied Hidden Semi-Markov Model
Type of publication: Journal paper
Citation: Tanwani_RA-L_2016
Publication status: Published
Journal: IEEE Robotics and Automation Letters
Volume: 1
Number: 1
Year: 2016
Month: January
Pages: 235-242
ISSN: 2377-3766
URL: http://ieeexplore.ieee.org/xpl...
DOI: 10.1109/LRA.2016.2517825
Abstract: In this paper, we investigate the semi-tied Gaussian mixture models for robust learning and adaptation of robot manipulation tasks. We make use of the spatial and temporal correlation in the data by tying the covariance matrices of the mixture model with common synergistic directions/basis vectors, instead of estimating full covariance matrices for each cluster in the mixture. This allows the reuse of the discovered synergies in different parts of the task having similar coordination patterns. We extend the approach to task-parameterized and hidden semi-Markov models for autonomous adaptation to changing environmental situations. The planned movement sequence from the model is smoothly followed with a finite horizon linear quadratic tracking controller. Experiments to encode whole body motion data in simulation, followed by valve opening and pick-and-place via obstacle avoidance tasks with the Baxter robot, show improvement over standard Gaussian mixture models wit h much less parameters and better generalization ability.
Keywords: dexterous manipulation, Learning and Adaptive Systems, Probability and Statistical Methods, Telerobotics and Teleoperation
Projects Idiap
Authors Tanwani, Ajay Kumar
Calinon, Sylvain
Editors Kulic, D.
Lee, D.
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  • Tanwani_RA-L_2016.pdf