Interactive Bayesian identification of kinematic mechanisms
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Kaelbling_Interactive bayesian.pdf
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1.05 MB
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Author(s)
Barragan, Patrick R.
Lozano-Perez, Tomas
Kaelbling, Leslie P.
Date Issued
May 2014
Journal
Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Barragan, Patrick R., Leslie Pack Kaelbling, and Tomas Lozano-Perez. “Interactive Bayesian Identification of Kinematic Mechanisms.” 2014 IEEE International Conference on Robotics and Automation (ICRA) (May 2014).
Version
Author's final manuscript
Abstract
This paper addresses the problem of identifying mechanisms based on data gathered while interacting with them. We present a decision-theoretic formulation of this problem, using Bayesian filtering techniques to maintain a distributional estimate of the mechanism type and parameters. In order to reduce the amount of interaction required to arrive at a confident identification, we select actions explicitly to reduce entropy in the current estimate. We demonstrate the approach on a domain with four primitive and two composite mechanisms. The results show that this approach can correctly identify complex mechanisms including mechanisms which are difficult to model analytically. The results also show that entropy-based action selection can significantly decrease the number of actions required to gather the same information.
MIT Department
Massachusetts Institute of Technology. Materials Processing Center
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/ICRA.2014.6907126
