  Numerous algorithms have been proposed to allow legged robots to learn to
walk. However, the vast majority of these algorithms is devised to learn to
walk 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 com-bines 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 con-trollers that allows the robot to reach every point
in its reachable space. Overall, TBR-Evolution opens a new kind of learning
algorithm that simultaneously optimizes all the achievable behaviors of a
robot.
