aiqbench analysis
This research introduces Terrain-conditioned Generative Motion Priors (T-GMP), a novel module that learns latent motion manifolds to enable humanoid robots to adapt naturally to diverse terrains. By integrating these priors into an adversarial learning pipeline with dynamic naturalness constraints, the method significantly improves traversal success and motion diversity compared to existing reinforcement learning baselines. The approach represents a meaningful advancement in generative models for robotic locomotion, addressing the critical challenge of balancing anthropomorphic fluidity with physical safety on complex surfaces.
T-GMP: Terrain-conditioned Generative Motion Priors for Versatile and Natural Humanoid Locomotion
arXiv:2606.06944v2 Announce Type: replace 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.
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.
Robotics (cs.RO)
arXiv:2606.06944 [cs.RO]
(or arXiv:2606.06944v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.06944 Focus to learn more arXiv-issued DOI via DataCite
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