Humanoid-VLA：面向具备视觉整合能力的通用人形机器人控制
Humanoid-VLA: Towards Universal Humanoid Control with Visual Integration
AI总结：
针对人形机器人控制框架依赖反应式机制、缺自主交互能力的问题，提出整合语言、第一视角视觉与运动控制的Humanoid-VLA框架，通过预对齐、视频微调及自监督数据增强，实现更强的通用类人控制与交互能力。
摘要：
本文针对当前人形机器人控制框架的局限性展开研究——这类框架主要依赖反应式机制，且因数据稀缺缺乏自主交互能力。我们提出Humanoid-VLA，这是一种整合了语言理解、第一视角场景感知与运动控制的新型框架，可实现通用人形机器人控制。Humanoid-VLA首先利用搭配文本描述的非第一视角人体运动数据集进行语言-运动预对齐，使模型能够学习通用运动模式与动作语义。随后我们通过参数高效的视频条件微调引入第一视角视觉上下文，实现具备上下文感知能力的运动生成。此外，我们提出一种自监督数据增强策略，可直接从运动数据中自动生成伪标注，该过程将原始运动序列转化为有价值的问答对，助力大规模无标注视频数据的有效利用。基于全身控制架构构建的Humanoid-VLA经大量实验验证，可完成物体交互与环境探索任务，且上下文感知能力更强，展现出更类人的自适应智能交互能力。
英文摘要：
This paper addresses the limitations of current humanoid robot control frameworks, which primarily rely on reactive mechanisms and lack autonomous interaction capabilities due to data scarcity. We propose Humanoid-VLA, a novel framework that integrates language understanding, egocentric scene perception, and motion control, enabling universal humanoid control. Humanoid-VLA begins with language-motion pre-alignment using non-egocentric human motion datasets paired with textual descriptions, allowing the model to learn universal motion patterns and action semantics. We then incorporate egocentric visual context through a parameter efficient video-conditioned fine-tuning, enabling context-aware motion generation. Furthermore, we introduce a self-supervised data augmentation strategy that automatically generates pseudoannotations directly derived from motion data. This process converts raw motion sequences into informative question-answer pairs, facilitating the effective use of large-scale unlabeled video data. Built upon whole-body control architectures, extensive experiments show that Humanoid-VLA achieves object interaction and environment exploration tasks with enhanced contextual awareness, demonstrating a more human-like capacity for adaptive and intelligent engagement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
