外观
论文HF Daily Papers · arXiv 2610.341864 天前
HuGe模型融合2000小时第一人称数据提升G1操作成功率
HuGe通用VLA模型预训练超2000小时人类与机器人数据，通过三阶段训练管线解决具身差异。在Unitree G1实机验证中，结合视觉适配的第一人称演示数据，显著提升了抓取放置任务成功率，刷新HumanoidArena仿真基准。
宇树 G1本文由 AI 根据公开内容整理，可能有误，请以原文为准：HuGe: Scaling Humanoid Generalist Vision-Language-Action Models with Egocentric Experience（HF Daily Papers · arXiv 2610.34186）
核心内容
针对人形机器人VLA模型面临全身控制难及真机数据昂贵痛点，研究提出HuGe通用模型家族。该模型基于超2000小时第一人称人类与机器人混合数据进行预训练，采用三阶段训练管线涵盖自我中心预训练、人形适配及下游微调。实验表明其在HumanoidArena仿真基准达到SOTA，并在Unitree G1实机Pick-and-Drop任务中证实了跨具身迁移的有效性。
技术架构上，HuGe利用三种主流VLA预测潜在运动Token驱动预训练全身控制器及手部信号。为解决视觉具身差异与本体感知缺失，预训练阶段通过仿真回放构建伪本体感知状态，微调阶段对第一人称观测进行手臂重渲染。这种数据增强策略使模型能有效利用海量人类视频经验，弥补纯机器人数据在泛化性上的不足，实现从人类经验到机器人策略的高效迁移。
关键看点
- 数据规模：预训练数据集包含超2000小时第一人称人类与机器人混合数据，支撑通用VLA学习
- 跨具身迁移：通过仿真回放构建伪本体感知状态，并在微调期重渲染手臂外观以消除视觉差异
- 控制架构：VLA输出潜在运动Token驱动预训练全身控制器，同步生成手部控制信号实现协调
- 实机验证：在Unitree G1上验证，融合视觉适配的人类演示数据显著提升抓取放置任务成功率
工程与产业观察
HuGe验证了利用海量第一人称人类视频数据训练人形机器人VLA的可行性。通过伪本体感知构建与视觉重渲染解决跨具身鸿沟，为缓解真机数据稀缺提供了工程范式。该方案在G1上的成功表明，将人类经验转化为机器人策略是提升具身智能泛化能力的关键路径，对降低数据采集成本具有参考价值。
英文摘要 (Abstract)
Extending Vision-Language-Action models (VLAs) to humanoid robots requires coordinated whole-body control and diverse demonstrations that are costly to collect in real-world scenarios. Although egocentric human demonstrations provide a complementary source of task experience, differences in visual embodiment and missing robot proprioception complicate transfer to humanoid policies. To address these challenges, we introduce HuGe, a family of Humanoid Generalist VLAs pre-trained on over 2,000 hours of egocentric human and robot data. Specifically, HuGe employs three existing representative VLA architectures to predict latent motion tokens for a pretrained whole-body controller, together with hand-control signals. Based on this, we introduce a three-stage training pipeline that combines egocentric pre-training, humanoid adaptation, and downstream fine-tuning. To facilitate transfer across embodiments, we construct pseudo-proprioceptive states through simulation playback during pre-training and modify arm appearance in egocentric observations through re-rendering during downstream fine-tuning. With this pipeline, HuGe achieves the state-of-the-art on simulation benchmark HumanoidArena. Furthermore, real-world experiments on the Unitree G1 indicate that augmenting robot demonstrations with visually adapted egocentric demonstrations significantly increases success on a pick-and-drop task.
Hugging Face Daily Papers 热度：1 upvotes 作者：Yang Liu, Jiacheng Liu, Hengtao Li, Yuheng Huang, Zijian Yang, Ji Dai, Maoxin Wang, Yi Gao, Qishen Wang, Baohua Yin, Peiyang Li, Shuanghao Bai, Mingyang Sun, Pengxiang Ding, Jianqin Yin, Lixin Yang, D…
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