导读
英文标题：Perceptive Humanoid Motion from Simulation arXiv编号：2611.06908 作者：Siyuan Hu, Abulikemu Abuduweili, Yue Meng 来源：arXiv 原文链接：https://arxiv.org/abs/26…
【编辑部】这对你意味着什么
该论文介绍了一种名为 HALO 的类人机器人自主系统，能够在模拟环境中合成视觉-语言-动作数据并在真实世界零样本迁移，实测在六个未建图真实环境的 360 次试验中成功率达 91%。目前成果仍处于学术研究与原型验证阶段，尚未形成面向消费者的产品或服务，因此对在美国生活的华人读者（留学生、新移民、在美工作者及其家庭）在签证移民、就业、收入物价、教育、安全、家庭生活等方面暂无直接可感知的影响。
英文标题：Perceptive Humanoid Motion from Simulation
arXiv编号：2611.06908
作者：Siyuan Hu, Abulikemu Abuduweili, Yue Meng
来源：arXiv
原文链接：https://arxiv.org/abs/2611.06908
中文摘要：
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 https://apple-aiml-research.github.io/ml-halo/ for code and videos.
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【编辑部】今日讨论
你在日常生活或工作中，最希望家用机器人率先帮你解决哪一类体力任务（如搬运、清洁、照看老人/小孩等）？
