HALO 人形机器人系统通过仿真合成数据实现 91% 真实环境零样本成功率

Perceptive Humanoid Motion from Simulation

AI 导读

研究团队提出人形自主系统 HALO，用跨一千个程序生成住宅的视觉-语言-运动数据管线替代人工遥操作采集，让数据规模随算力而非人力扩展。HALO 学会坐下、寻物导航、侧步过半开门、桌下蹲爬和推箱等技能并组合完成长时程任务，训练中从未见过真实住宅，在六个未测绘真实环境的 360 次试验中取得 91% 成功率，代码和视频已开源。

推荐理由

原文给出从零真实环境训练到 91% 零样本迁移成功率的具体数据，读者可据此评估仿真合成数据路线在具身智能上的可行性。

正文 · 原文

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Abstract:Humanoid robots have the potential to automate a wide range of physical tasks. Progress toward generalist humanoid autonomy, however, remains constrained by a data bottleneck: mapping pixels and free-form language to low-level joint commands requires paired supervision, and no corpus of such data exists at sufficiently large scale. Some approaches rely on human teleoperation and wearable capture devices, which require at least an hour of human time to produce an hour of data. We present HALO, a humanoid autonomy system powered by a pipeline that synthesizes vision-language-motion data across a thousand procedurally generated homes. The corpus scales with compute rather than human labor. HALO acquires a repertoire of physical skills, including sitting on chairs, navigating to named objects, stepping sideways through half-open doors, crouching and crawling beneath tables, and pushing boxes. It orchestrates these skills to solve composite, long-horizon tasks. Without ever seeing a real home during training, HALO transfers zero-shot to the physical world, achieving a 91% success rate across 360 trials in six unmapped real-world environments. See this https URL for code and videos.

Comments:	Code and videos at: this https URL
Subjects:	Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as:	arXiv:2611.06908 [cs.RO]
	(or arXiv:2611.06908v1 [cs.RO] for this version)
	https://doi.org/10.48550/arXiv.2611.06908 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Matthias Müller [view email]
[v1] Wed, 4 Nov 2026 18:55:33 UTC (14,644 KB)