ALLSTEPS: Curriculum‐driven Learning of Stepping Stone Skills
Zhaoming Xie, Hung Yu Ling, Nam Hee Kim, Michiel van de Panne
Abstract
Abstract Humans are highly adept at walking in environments with foot placement constraints, including stepping‐stone scenarios where footstep locations are fully constrained. Finding good solutions to stepping‐stone locomotion is a longstanding and fundamental challenge for animation and robotics. We present fully learned solutions to this difficult problem using reinforcement learning. We demonstrate the importance of a curriculum for efficient learning and evaluate four possible curriculum choices compared to a non‐curriculum baseline. Results are presented for a simulated humanoid, a realistic bipedal robot simulation and a monster character, in each case producing robust, plausible motions for challenging stepping stone sequences and terrains.