人类视频教机器人，成功率从8%到52%
机器人学新技能，过去得让工程师穿着动捕服一遍遍示范，贵且慢。这篇让机器人直接看人类的示范视频学，但难点在于人和机器人的身体差太远——手不一样、视角不一样、动作空间也不一样，而底层模型又主要是在机器人数据上预训练的，直接混着训往往学废。H2RB 的做法是在两个接口上分别搭桥：观察端用几何锚定把人类和机器人的画面映射到同一套空间参照里，动作端用解耦解码器让两边共享动作的隐表示、却不强行套用同一个动作映射，这样人类示范能提供行为监督，又不破坏预训练模型原有的视觉、语义和控制先验。实测里，物体层面的迁移从 8.0 提到 51.8，技能迁移拿到 62.7，而普通联合训练基线完全学不会人类示范的技能；物体摆放位置变化时，分布外迁移也从 6.0 提到 45.3。它不是你明天能用上的东西，但它指向一条路：人类视频可能成为适配机器人策略的廉价监督来源，少依赖昂贵的真机数据采集。
📄 原文摘要(英文)
Pretrained Vision-Language-Action (VLA) policies provide a strong starting point for deployment, but adapting them to a target robot and environment still requires costly embodiment-specific demonstrations. Human demonstrations are far easier to collect, yet directly leveraging them is challenging due to the substantial human-robot embodiment gap, particularly when the underlying VLA has been pretrained primarily on robot data. We introduce H2RB (Human-To-Robot Bridge), a human-robot co-training framework for adapting pretrained VLA policies from unpaired human and robot demonstrations. H2RB bridges the embodiment gap at both interfaces: geometry-anchored alignment grounds human and robot observations in a compatible representation, while a decoupled-decoder tokenizer shares action latents without forcing both domains through a single action mapping. This design allows human demonstrations to provide useful behavioral supervision while retaining the rich visual, semantic, and control priors of the pretrained VLA. Across real-world tasks, H2RB substantially improves object transfer performance from 8.0 to 51.8 and enables skill transfer to score 62.7, in settings where co-train baselines totally fail to transfer human-demonstrated skills. The benefit also persists under object-placement shifts, improving out-of-distribution transfer from 6.0 to 45.3. H2RB suggests a way for human demonstrations to serve as a scalable supervision source for adapting pretrained robotic policies, reducing the dependence on costly target-robot data collection.