Apple 发布 HALO：纯仿真训练的人形机器人零样本迁移现实世界
Perceptive Humanoid Motion from Simulation
Apple 教机器人只在虚拟家里练，搬到真实房间还能 360 次测试拿 91% 成功率，做法挺新鲜。
Apple 推出人形机器人自主系统 HALO，核心是一条在 1000 个程序化生成家庭场景中合成视觉-语言-动作数据的流水线，数据规模随算力而非人力增长。HALO 学会了坐椅子、走向指定物体、侧身穿过半开的门、在桌下匍匐、推箱子等技能，并能编排这些技能完成长时程复合任务。训练全程未见过真实家庭环境，HALO 在 6 个未建图的真实环境中进行 360 次测试，成功率达到 91%。代码和视频已在项目页面开源。
Perceptive Humanoid Motion from Simulation
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.