教机器人学人干活，关键在“翻译”动作
想让机器人像人一样灵巧地操作物体，最直接的想法是录下人的动作，让机器人模仿。但问题在于：人的手有5根手指，机器人只有两个夹爪，直接模仿就像让一个钳工去弹钢琴。这篇论文的解法很聪明——他们不学人的完整动作，只学“手腕在头部相机视野里的相对平移”。这个信号人和机器人都有，而且不受手指差异的干扰。他们再用一个能处理不同“身体”的AI模型，把视觉、语言和动作指令混在一起训练。结果在双手机器人任务上，这种“翻译”后的动作比直接模仿人的6自由度姿态有效得多，而且数据越多效果越好。它不是你明天就能用的技术，但给了一个新思路：与其让机器人硬学人的动作，不如先找到人和机器共通的“动作语言”。
📄 原文摘要(英文)
We study whether we can learn novel manipulation skills from human actions to a bi-manual robot with parallel grippers. Human action data is cheap, abundant, and diverse, making it one of the most promising resources for scaling up robot learning. Yet transferring skills from humans to robots remains hard: most prior work treats humans as just another bi-manual 6DoF embodiment, where hand-pose estimates are noisy and the contact patterns of human fingers differ fundamentally from those of a parallel gripper. We argue that learning rotation-inclusive action signals from human data is therefore sub-optimal, and instead propose a bridging action representation: the relative wrist translation within the initial head-camera frame, an action space shared by humans and robots. To handle the potential absence of certain action components in different embodiments, we build a π_0-like vision-language-action model with interleaved action tokens and attention masking. On a suite of novel bi-manual manipulation tasks, our bridging action transfers human manipulation knowledge to robots far more effectively than noisy 6DoF human actions and scales with the amount of human data.