In this section
@article{Cully:2016:10.1162/EVCO_a_00143,
author = {Cully, A and Mouret, J-B},
doi = {10.1162/EVCO_a_00143},
journal = {Evolutionary Computation},
pages = {59--88},
title = {Evolving a behavioral repertoire for a walking robot},
url = {http://dx.doi.org/10.1162/EVCO_a_00143},
volume = {24},
year = {2016}
}
TY - JOUR
AB - Numerous algorithms have been proposed to allow legged robots to learn to walk.However, most of these algorithms are devised to learn walking in a straight line,which is not sufficient to accomplish any real-world mission. Here we introduce theTransferability-based Behavioral Repertoire Evolution algorithm (TBR-Evolution), anovel evolutionary algorithm that simultaneously discovers several hundreds of simplewalking controllers, one for each possible direction. By taking advantage of solutionsthat are usually discarded by evolutionary processes, TBR-Evolution is substantiallyfaster than independently evolving each controller. Our technique relies on two meth-ods: (1) novelty search with local competition, which searches for both high-performingand diverse solutions, and (2) the transferability approach, which combines simulationsand real tests to evolve controllers for a physical robot. We evaluate this new techniqueon a hexapod robot. Results show that with only a few dozen short experiments per-formed on the robot, the algorithm learns a repertoire of controllers that allows therobot to reach every point in its reachable space. Overall, TBR-Evolution introduceda new kind of learning algorithm that simultaneously optimizes all the achievablebehaviors of a robot.
AU - Cully,A
AU - Mouret,J-B
DO - 10.1162/EVCO_a_00143
EP - 88
PY - 2016///
SN - 1063-6560
SP - 59
TI - Evolving a behavioral repertoire for a walking robot
T2 - Evolutionary Computation
UR - http://dx.doi.org/10.1162/EVCO_a_00143
UR - http://hdl.handle.net/10044/1/50402
VL - 24
ER -
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