HAF-VLA: Hierarchical Action Flow Framework for Humanoid Vision-Language-Action Control

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为 VLA、具身智能、人形机器人控制、机器人操作等高价值论文补充中文说明。

Original abstract

Humanoid robots hold great promise as general-purpose agents in human-centered environments, yet adapting generalist vision-language-action (VLA) policies to real-world humanoid loco-manipulation remains challenging. Unlike fixed-base or wheeled manipulators, humanoids must coordinate locomotion, posture, gaze, and dual-arm manipulation over a high-dimensional whole-body action space, where inaccurate base or waist motions can easily induce unstable and aggressive upper-body compensation. We propose HAF-VLA, a hierarchical action-flow framework for humanoid VLA control. Built upon a pretrained flow-matching VLA, HAF-VLA inherits the vision-language prefix encoder and action expert weights, and adapts them to humanoid whole-body loco-manipulation with learnable stage embeddings and cross-stage KV-cache conditioning. Instead of generating the full action chunk in a single pass, HAF-VLA progressively denoises a shared whole-body action space: first locomotion and head commands, then waist refinement, and finally full-body manipulation. To preserve structural dependencies across stages, we introduce a cross-stage KV cache conditioning mechanism, enabling subsequent action generation stages to refine and augment their outputs based on the action representations from preceding stages. Experimental results across six real-world tasks demonstrate that our framework significantly enhances the performance of existing models in humanoid loco-manipulation tasks, demonstrating an effective pathway for adapting strong mobile-manipulation VLAs to humanoid whole-body loco-manipulation.

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• Official / arXiv page
• PDF from original source

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