Automated Gait Adaptation for Legged Robots

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General Robotics, Automation, Sensing and Perception Laboratory
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Weingarten, Joel D
Lopes, Gabriel A. D.
Buehler, Martin
Groff, Richard E
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Gait parameter adaptation on a physical robot is an error-prone, tedious and time-consuming process. In this paper we present a system for gait adaptation in our RHex series of hexapedal robots that renders this arduous process nearly autonomous. The robot adapts its gait parameters by recourse to a modified version of Nelder-Mead descent while managing its self-experiments and measuring the outcome by visual servoing within a partially engineered environment. The resulting performance gains extend considerably beyond what we have managed with hand tuning. For example, the hest hand tuned alternating tripod gaits never exceeded 0.8 m/s nor achieved specific resistance helow 2.0. In contrast, Nelder-Mead based tuning has yielded alternating tripod gaits at 2.7 m/s (well over 5 body lengths per second) and reduced specific resistance to 0.6 while requiring little human intervention at low and moderate speeds. Comparable gains have been achieved on the much larger ruggedized version of this machine.

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2004-04-26
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2023-05-16T22:30:07.000
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Copyright 2004 IEEE. Reprinted from Proceedings of the 2004 IEEE International Conference on Robotics and Automation (ICRA 2004), Volume 3, pages 2153-2158. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. NOTE: At the time of publication, author Daniel Koditschek was affiliated with the University of Michigan. Currently (August 2005), he is a faculty member in the Department of Electrical and Systems Engineering at the University of Pennsylvania.
Copyright 2004 IEEE. Reprinted from Proceedings of the IEEE International Conference on Robotics and Automation 2004 (ICRA 2004), pages 2153-2158. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. NOTE: At the time of publication, author Daniel Koditschek was affiliated with the University of Michigan. Currently (June 2005), he is a faculty member in the Department of Electrical and Systems Engineering at the University of Pennsylvania.
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