Commanding Humanoid by Free-form Language: A Large Language Action Model with Unified Motion Vocabulary

• URL: http://arxiv.org/abs/2511.22963v1
• Date: Fri, 28 Nov 2025 08:11:24 GMT
• Title: Commanding Humanoid by Free-form Language: A Large Language Action Model with Unified Motion Vocabulary
• Authors: Zhirui Liu, Kaiyang Ji, Ke Yang, Jingyi Yu, Ye Shi, Jingya Wang,
• Abstract summary: We introduce Humanoid-LLA, a Large Language Action Model that maps expressive language commands to physically executable whole-body actions for humanoid robots.<n>Our approach integrates three core components: a unified motion vocabulary that aligns human and humanoid motion primitives into a shared discrete space; a vocabulary-directed controller distilled from a privileged policy to ensure physical feasibility; and a physics-informed fine-tuning stage using reinforcement learning with dynamics-aware rewards to enhance robustness and stability.
• Score: 59.98573566227095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Enabling humanoid robots to follow free-form language commands is critical for seamless human-robot interaction, collaborative task execution, and general-purpose embodied intelligence. While recent advances have improved low-level humanoid locomotion and robot manipulation, language-conditioned whole-body control remains a significant challenge. Existing methods are often limited to simple instructions and sacrifice either motion diversity or physical plausibility. To address this, we introduce Humanoid-LLA, a Large Language Action Model that maps expressive language commands to physically executable whole-body actions for humanoid robots. Our approach integrates three core components: a unified motion vocabulary that aligns human and humanoid motion primitives into a shared discrete space; a vocabulary-directed controller distilled from a privileged policy to ensure physical feasibility; and a physics-informed fine-tuning stage using reinforcement learning with dynamics-aware rewards to enhance robustness and stability. Extensive evaluations in simulation and on a real-world Unitree G1 humanoid show that Humanoid-LLA delivers strong language generalization while maintaining high physical fidelity, outperforming existing language-conditioned controllers in motion naturalness, stability, and execution success rate.

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