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arXiv · 2511.19236

SENTINEL: A Fully End-to-End Language-Action Model for Humanoid Whole Body Control

Yuxuan Wang·Haobin Jiang·Shiqing Yao·Ziluo Ding·Zongqing Lu
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Abstract

Existing humanoid control systems often rely on teleoperation or modular generation pipelines that separate language understanding from physical execution. However, the former is entirely human-driven, and the latter lacks tight alignment between language commands and physical behaviors. In this paper, we present SENTINEL, a fully end-to-end language-action model for humanoid whole-body control. We construct a large-scale dataset by tracking human motions in simulation using a pretrained whole body controller, combined with their text annotations. The model directly maps language commands and proprioceptive inputs to low-level actions without any intermediate representation. The model generates action chunks using flow matching, which can be subsequently refined by a residual action head for real-world deployment. Our method exhibits strong semantic understanding and stable execution on humanoid robots in both simulation and real-world deployment, and also supports multi-modal extensions by converting inputs into texts.

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Yuxuan Wang, Haobin Jiang, Shiqing Yao, Ziluo Ding, Zongqing Lu. 2025-11-24. SENTINEL: A Fully End-to-End Language-Action Model for Humanoid Whole Body Control. https://arxiv.org/abs/2511.19236

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