T-GMP: Terrain-conditioned Generative Motion Priors for Versatile and Natural Humanoid Locomotion

• URL: http://arxiv.org/abs/2606.06944v1
• Date: Fri, 05 Jun 2026 06:15:42 GMT
• Title: T-GMP: Terrain-conditioned Generative Motion Priors for Versatile and Natural Humanoid Locomotion
• Abstract summary: We propose a module that captures a terrain-conditioned latent motion manifold from a few expert state-terrain demonstrations.<n>The learned priors enable smooth style transitions, facilitating a unified policy that adapts to terrain variations.<n>We integrate T-GMP into an adversarial learning pipeline with our proposed Foothold Penalty, where a discriminator dynamically modulates naturalness constraints conditioned on local terrain features.
• Score: 10.258225375602231
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Achieving both anthropomorphic naturalness and robust terrain traversal remains a fundamental challenge in humanoid locomotion. Existing Reinforcement Learning (RL) 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 using a Conditional Variational Autoencoder (CVAE). 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 with our proposed Foothold Penalty, where a discriminator dynamically modulates naturalness constraints conditioned on local terrain features, guiding the generation of versatile and human-like motions. Experimental results demonstrate that our method outperforms existing baselines in traversal success rate and motion smoothness, while preserving biomimetically natural and physically coordinated motions.

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