aiqbench analysis
This research introduces T-GMP, a terrain-conditioned generative module that overcomes the adaptability limits of fixed motion priors in reinforcement learning-based humanoid locomotion. By leveraging a Conditional Variational Autoencoder and an adversarial learning pipeline with a dynamic foothold penalty, the method achieves superior traversal success and motion smoothness across varying environments. The approach effectively balances robust terrain navigation with biomimetic naturalness, addressing a core challenge in next-generation robot mobility.
T-GMP: Terrain-conditioned Generative Motion Priors for Versatile and Natural Humanoid Locomotion
arXiv:2606.06944v1 Announce Type: new 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.
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.
Robotics (cs.RO)
arXiv:2606.06944 [cs.RO]
(or arXiv:2606.06944v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.06944 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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