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Learning for autonomous navigation : extrapolating from underfoot to the far fieldAutonomous off-road navigation of robotic ground vehicles has important applications on Earth and in space exploration. Progress in this domain has been retarded by the limited lookahead range of 3-D sensors and by the difficulty of preprogramming systems to understand the traversability of the wide variety of terrain they can encounter. Enabling robots to learn from experience may alleviate both of these problems. We define two paradigms for this, learning from 3-D geometry and learning from proprioception, and describe initial instantiations of them we have developed under DARPA and NASA programs. Field test results show promise for learning traversability of vegetated terrain, learning to extend the lookahead range of the vision system, and learning how slip varies with slope.
Document ID
20060043857
Acquisition Source
Jet Propulsion Laboratory
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Matthies, Larry
Turmon, Michael
Howard, Andrew
Angelova, Anelia
Tang, Benyang
Mjolsness, Eric
Date Acquired
August 23, 2013
Publication Date
January 1, 2005
Publication Information
Publication: Journal of Machine Learning Research
Volume: 1
Distribution Limits
Public
Copyright
Other
Keywords
autonomous robots
vision systems

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