Online adaptation of locomotion with evolutionary algorithms: a transferability-based approach
Pages 817 - 818
Abstract
Wheel-legged hybrid robots are versatile machines that can employ several locomotion modes; however, automatically choosing the right locomotion mode is still an open problem in robotics. We here propose that the robot autonomously discovers its locomotion mode using a multi-objective evolutionary optimization and a fixed internal model. Three objectives are optimized: (1) the displacement speed computed with the internal model, (2) the predicted expended energy and (3) the transferability score, which reflects how well the behavior of the real robot is in agreement with the predictions of the internal model. This transferability function is actively learned by conducting 20 experiments on the real robot during the optimization. We tested this approach with a wheel-legged robot in three situations (flat ground, grass-like terrain, tunnel-like environment): in each case, the evolutionary algorithm found efficient controllers for forward locomotion in 1 to 2 minutes.
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Index Terms
- Online adaptation of locomotion with evolutionary algorithms: a transferability-based approach
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Published In
July 2011
1548 pages
Copyright © 2011 Authors.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 12 July 2011
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GECCO '11
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