  Humans are highly adept at walking in environments with foot placement
constraints, including stepping-stone scenarios where the 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 human character, a realistic
bipedal robot simulation and a monster character, in each case producing
robust, plausible motions for challenging stepping stone sequences and
terrains.
