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- Title
Evolving a Behavioral Repertoire for a Walking Robot.
- Authors
Cully, A.; Mouret, J.-B.
- Abstract
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 the Transferability-based Behavioral Repertoire Evolution algorithm (TBR-Evolution), a novel evolutionary algorithm that simultaneously discovers several hundreds of simple walking controllers, one for each possible direction. By taking advantage of solutions that are usually discarded by evolutionary processes, TBR-Evolution is substantially faster than independently evolving each controller. Our technique relies on two methods: (1) novelty search with local competition, which searches for both high-performing and diverse solutions, and (2) the transferability approach, which combines simulations and real tests to evolve controllers for a physical robot. We evaluate this new technique on a hexapod robot. Results show that with only a few dozen short experiments performed on the robot, the algorithm learns a repertoire of controllers that allows the robot to reach every point in its reachable space. Overall, TBR-Evolution introduced a new kind of learning algorithm that simultaneously optimizes all the achievable behaviors of a robot.
- Subjects
ROBOTS; EVOLUTIONARY algorithms; BEHAVIOR evolution; SOFTWARE compatibility; MACHINE learning; SIMULATION methods &; models
- Publication
Evolutionary Computation, 2016, Vol 24, Issue 1, p59
- ISSN
1063-6560
- Publication type
Article
- DOI
10.1162/EVCO_a_00143