arXiv cs.AI· Siyuan Hu, Abulikemu Abuduweili, Yue Meng, Shipeng Liu, Quentin Leboutet, Chen Chen, Matthias M\"uller, Vladlen Koltun·· 11 小时前AI 评分63
HALO：从仿真合成数据训练人形机器人实现零样本真实环境迁移
Perceptive Humanoid Motion from Simulation
AI 导读
研究团队提出 HALO 人形机器人自主系统，通过程序化生成的约一千套住宅合成视觉-语言-动作数据，数据规模随算力扩展而非依赖人工采集。HALO 习得坐椅子、导航到指定物体、侧身穿半开门、蹲爬桌底和推箱子等技能，并能组合完成长程任务。训练中从未见过真实住宅，HALO 仍零样本迁移到物理世界，在六个未建图的真实环境中完成 360 次试验，成功率达 91%。
正文 · 原文
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)
来源：arXiv cs.AI · arxiv.org