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

2026-06-05

AI Summary

This work addresses the challenge of enabling humanoid robots to achieve both human-like naturalness and robust locomotion over complex terrains. To this end, the authors propose Terrain-conditioned Generative Motion Prior (T-GMP), a novel approach that integrates terrain information directly into motion prior learning. T-GMP employs a conditional variational autoencoder to model terrain-dependent latent motion manifolds from a limited set of expert demonstrations. By incorporating adversarial training, a foot-placement penalty mechanism, and a terrain-aware discriminator, the method enables a unified policy to generate adaptive and diverse human-like gaits. Experimental results demonstrate that T-GMP outperforms existing methods in terms of traversal success rate and motion smoothness while preserving strong biomechanical naturalness and physical coordination.

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.

Problem

• humanoid locomotion
• motion naturalness
• terrain adaptability
• reinforcement learning
• motion priors

Innovation

• Terrain-conditioned Motion Priors
• Conditional Variational Autoencoder
• Adversarial Learning
• Humanoid Locomotion
• Foothold Penalty