Learning Whole-Body Humanoid Locomotion via Motion Generation and Motion Tracking

2026-04-19

AI Summary

This work addresses the challenges of high-dimensional control, postural instability, and real-time perceptual demands faced by humanoid robots operating on complex terrains. To this end, the authors propose a whole-body motion control framework that integrates diffusion models with reinforcement learning. The approach uniquely combines real-time terrain-aware diffusion-based motion generation with a reinforcement learning-based tracking controller, augmented by a closed-loop fine-tuning mechanism to enable online adaptation of reference motions and coordinated whole-body responses. Evaluated on the Unitree G1 platform, the system successfully executes diverse locomotion tasks—including stepping over boxes, traversing rails, climbing stairs, and navigating mixed-terrain environments—demonstrating significantly enhanced motion generalization and robustness.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Computer Vision: Diffusion Models for Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Whole-body humanoid locomotion is challenging due to high-dimensional control, morphological instability, and the need for real-time adaptation to various terrains using onboard perception. Directly applying reinforcement learning (RL) with reward shaping to humanoid locomotion often leads to lower-body-dominated behaviors, whereas imitation-based RL can learn more coordinated whole-body skills but is typically limited to replaying reference motions without a mechanism to adapt them online from perception for terrain-aware locomotion. To address this gap, we propose a whole-body humanoid locomotion framework that combines skills learned from reference motions with terrain-aware adaptation. We first train a diffusion model on retargeted human motions for real-time prediction of terrain-aware reference motions. Concurrently, we train a whole-body reference tracker with RL using this motion data. To improve robustness under imperfectly generated references, we further fine-tune the tracker with a frozen motion generator in a closed-loop setting. The resulting system supports directional goal-reaching control with terrain-aware whole-body adaptation, and can be deployed on a Unitree G1 humanoid robot with onboard perception and computation. The hardware experiments demonstrate successful traversal over boxes, hurdles, stairs, and mixed terrain combinations. Quantitative results further show the benefits of incorporating online motion generation and fine-tuning the motion tracker for improved generalization and robustness.

Problem

• humanoid locomotion
• whole-body control
• terrain-aware adaptation
• motion generation
• real-time perception

Innovation

• diffusion model
• whole-body locomotion
• terrain-aware adaptation
• reinforcement learning
• motion generation