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This research aims to enhance humanoid robot locomotion by integrating motion generation with adaptive tracking strategies.

May 7, 2026Open Access

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

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This research aims to enhance humanoid robot locomotion by integrating motion generation with adaptive tracking strategies.
Developed a framework for whole-body humanoid locomotion combining reinforcement learning and motion generation.
Trained a diffusion model on retargeted human motions for real-time terrain-aware reference motion generation.
Employed a closed-loop training method for fine-tuning a motion tracker using generated data.
Achieved robust locomotion over mixed terrain with adaptive control capabilities.
Demonstrated improved performance in goal-reaching tasks with effective terrain adaptation.
Quantitative analysis showed enhancements in generalization and robustness from integrated motion generation and fine-tuning.

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Proposed framework improves control and terrain adaptability in humanoid locomotion using reinforcement learning and diffusion models.

Zhang et al. (2026) studied this question.

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