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• W1965675101 abstract "Learning in robotics typically involves choosing a simple goal (e.g. walking) and assessing the performance of each controller with regard to this task (e.g. walking speed). However, learning advanced, input-driven controllers (e.g. walking in each direction) requires testing each controller on a large sample of the possible input signals. This costly process makes difficult to learn useful low-level controllers in robotics. Here we introduce BR-Evolution, a new evolutionary learning technique that generates a behavioral repertoire by taking advantage of the candidate solutions that are usually discarded. 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 distinguish similar controllers, it uses a performance objective that allows it to produce a collection of diverse but high-performing behaviors. We evaluated this new technique by evolving gait controllers for a simulated hexapod robot. Results show that a single run of the EA quickly finds a collection of controllers that allows the robot to reach each point of the reachable space. Overall, BR-Evolution opens a new kind of learning algorithm that simultaneously optimizes all the achievable behaviors of a robot." @default.
• W1965675101 created "2016-06-24" @default.
• W1965675101 creator A5011747084 @default.
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• W1965675101 date "2013-07-06" @default.
• W1965675101 modified "2023-09-30" @default.
• W1965675101 title "Behavioral repertoire learning in robotics" @default.
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• W1965675101 doi "https://doi.org/10.1145/2463372.2463399" @default.
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