Evolving Dynamic Locomotion Policies in Minutes

Many effective evolutionary methods have been proposed that allow robots to learn how to walk. Most of the proposed methods have one or more of the following drawbacks: (a) utilization of hand designed open loop policies that cannot scale to different robots, and/or (b) requiring big wall time due to sample inefficiency and simulation costs, a fact that limits the practical usage of those algorithms. In the paper at hand, we propose combination of (a) a simplified model for locomotion dynamics, and (b) the effectiveness of quality-diversity algorithms, and propose a novel algorithm that is able to evolve, in less than an hour on a standard computer, generic (e.g. neural network), and reactive locomotion policies. Our approach makes it possible to generate in a few minutes reactive policies for locomotion that can perform dynamic motions like jumps. We also present preliminary results of transferring the behaviors to realistic simulators using a whole body inverse kinematics solver and a joint impedance controller.