ALLSTEPS: Curriculum-driven Learning of Stepping Stone Skills

被引:53
|
作者
Xie, Zhaoming [1 ]
Ling, Hung Yu [1 ]
Kim, Nam Hee [1 ]
van de Panne, Michiel [1 ]
机构
[1] Univ British Columbia, Vancouver, BC, Canada
关键词
CCS Concepts; • Computing methodologies → Reinforcement learning; Physical simulation; WALKING;
D O I
10.1111/cgf.14115
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
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.
引用
收藏
页码:213 / 224
页数:12
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