Abstract:Achieving both anthropomorphic naturalness and rich motion diversity during terrain traversal remains a fundamental challenge in humanoid locomotion. Existing reinforcement learning approaches typically rely on fixed motion priors, limiting their adaptability to varying environments. We propose Terrain-conditioned Generative Motion Priors (T-GMP), a module that captures a terrain-conditioned latent motion manifold from a few expert state-terrain demonstrations. The learned priors enable smooth style transitions, facilitating a unified policy that adapts to terrain variations. We integrate T-GMP into an adversarial learning pipeline, where a discriminator dynamically modulates naturalness constraints conditioned on local terrain features, guiding the generation of versatile and human-like motions. We further introduce a Foothold Penalty to promote safe foot placement on challenging terrains. Experimental results demonstrate that T-GMP outperforms existing baselines in motion naturalness, motion diversity, and traversal success rates, while preserving physically coordinated motions.
From: Junhong Guo [view email]
[v1]
Fri, 5 Jun 2026 06:15:42 UTC (20,972 KB)
[v2]
Sat, 29 Aug 2026 04:53:14 UTC (22,858 KB)
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