ALLSTEPS: Curriculum-driven Learning of Stepping Stone Skills

• URL: http://arxiv.org/abs/2005.04323v2
• Date: Sun, 30 Aug 2020 00:45:00 GMT
• Title: ALLSTEPS: Curriculum-driven Learning of Stepping Stone Skills
• Authors: Zhaoming Xie, Hung Yu Ling, Nam Hee Kim, Michiel van de Panne
• Abstract summary: 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. Results are presented for a simulated human character, a realistic bipedal robot simulation and a monster character, in each case producing robust, plausible motions.
• Score: 8.406171678292964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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