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ALLSTEPS: Curriculum-driven Learning of Stepping Stone Skills

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arxiv 2005.04323 v2 pith:L6XTHKJ2 submitted 2020-05-09 cs.GR cs.LGcs.RO

classification cs.GRcs.LGcs.RO
keywords learningcharactercurriculumfullysolutionssteppingstepping-stonestone
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. HuMam: Humanoid Motion Control via End-to-End Deep Reinforcement Learning with Mamba

    cs.RO 2025-09 conditional novelty 5.0 of 10

    A single-layer Mamba encoder as the policy backbone improves learning speed, stability, and energy efficiency of an end-to-end RL humanoid walking controller in simulation compared to a feedforward baseline.

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