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Learning slip behavior using automatic mechanical supervision

Angelova, Anelia and Matthies, Larry and Helmick, Daniel and Perona, Pietro (2007) Learning slip behavior using automatic mechanical supervision. In: 2007 IEEE International Conference on Robotics and Automation. IEEE , Piscataway, NJ, pp. 1741-1748. ISBN 1-4244-0601-3.

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We address the problem of learning terrain traversability properties from visual input, using automatic mechanical supervision collected from sensors onboard an autonomous vehicle. We present a novel probabilistic framework in which the visual information and the mechanical supervision interact to learn particular terrain types and their properties. The proposed method is applied to learning of rover slippage from visual information in a completely automatic fashion. Our experiments show that using mechanical measurements as automatic supervision significantly improves the visual-based classification alone and approaches the results of learning with manual supervision. This work will enable the rover to drive safely on slopes, learning autonomously about different terrains and their slip characteristics.

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Perona, Pietro0000-0002-7583-5809
Additional Information:© 2007 IEEE. This research was carried out by the Jet Propulsion Labo- ratory, California Institute of Technology with funding from NASA’s Mars Technology Program. Thanks also to the JPL LAGR team for giving us access to the LAGR vehicle and to Nick Hudson for making us aware of reference [13].
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:60076
Deposited By: Caroline Murphy
Deposited On:16 Sep 2015 17:33
Last Modified:10 Nov 2021 22:29

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