BibTex format

author = {Cully, AHR and Mouret, J-B},
doi = {10.1145/2463372.2463399},
pages = {175--182},
publisher = {ACM},
title = {Behavioral repertoire learning in robotics},
url = {},
year = {2013}

RIS format (EndNote, RefMan)

AB - Behavioral Repertoire Learning in RoboticsAntoine CullyISIR, Université Pierre et Marie Curie-Paris 6,CNRS UMR 72224 place Jussieu, F-75252, Paris Cedex 05,Francecully@isir.upmc.frJean-Baptiste MouretISIR, Université Pierre et Marie Curie-Paris 6,CNRS UMR 72224 place Jussieu, F-75252, Paris Cedex 05,Francemouret@isir.upmc.frABSTRACTLearning in robotics typically involves choosing a simple goal(e.g. walking) and assessing the performance of each con-troller with regard to this task (e.g. walking speed). How-ever, learning advanced, input-driven controllers (e.g. walk-ing in each direction) requires testing each controller on alarge sample of the possible input signals. This costly pro-cess makes difficult to learn useful low-level controllers inrobotics.Here we introduce BR-Evolution, a new evolutionary learn-ing technique that generates a behavioral repertoire by tak-ing advantage of the candidate solutions that are usuallydiscarded. Instead of evolving a single, general controller,BR-evolution thus evolves a collection of simple controllers,one for each variant of the target behavior; to distinguishsimilar controllers, it uses a performance objective that al-lows it to produce a collection of diverse but high-performingbehaviors. We evaluated this new technique by evolving gaitcontrollers for a simulated hexapod robot. Results show thata single run of the EA quickly finds a collection of controllersthat allows the robot to reach each point of the reachablespace. Overall, BR-Evolution opens a new kind of learningalgorithm that simultaneously optimizes all the achievablebehaviors of a robot.
AU - Cully,AHR
AU - Mouret,J-B
DO - 10.1145/2463372.2463399
EP - 182
PY - 2013///
SP - 175
TI - Behavioral repertoire learning in robotics
UR -
UR -
ER -