Rapid Humanoid Motion Learning through Coordinated, Parallel Evolution.

Planning movements for humanoid robots is still a major challenge due to the very high degrees-of-freedom involved. Most humanoid control frameworks incorporate dynamical constraints related to a task that require detailed knowledge of the robot's dynamics, making them impractical as efficient planning. In previous work, we introduced a novel planning method that uses an inverse kinematics solver called Natural Gradient Inverse Kinematics (NGIK) to build task-relevant roadmaps (graphs in task space representing robot configurations that satisfy task constraints) by searching the configuration space via the Natural Evolution Strategies (NES) algorithm. The approach places minimal requirements on the constraints, allowing for complex planning in the task space. However, building a roadmap via NGIK is too slow for dynamic environments. In this paper, the approach is scaled-up to a fully-parallelized implementation where additional constraints coordinate the interaction between independent NES searches running on separate threads. Parallelization yields a 12x speedup that moves this promising planning method a major step closer to working in dynamic environments.