Transferability-based Behavioral Repertoire Learning in robotics
Published on Mar 21, 2013
Numerous algorithms have been proposed to allow legged robots to learn to walk. However, their vast majority are devised to learn to walk along a straight line, which not sufficient to accomplish any real-world mission.
Here we introduce TBR-Learning, a new learning algorithm that simultaneously discovers several hundreds of simple walking controllers, one for each possible direction. By taking advantage of solutions that are usually discarded, TBR-Learning is substantially faster than independently learning each controller. Our technique relies on two methods: (1) novelty search with local competition, which comes from the artificial life research field and (2) the transferability approach, which combines simulations and real tests to optimize a policy. We evaluate this new technique on a hexapod robot. Results show that with only a few dozens of short experiments performed on the physical robot, the algorithm learns a collection of controllers that allows the robot to reach each point of its reachable space. Overall, TBR-Learning opens a new kind of learning algorithm that simultaneously optimizes all the achievable behaviors of a robot.
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