Robotics faces a fundamental challenge of data scarcity. Unlike language or vision research, there is no internet-scale dataset for robotic manipulation. A promising path forward is to leverage egocentric human data, which can be collected more easily, with greater breadth, and at a larger scale. Towards this end, we investigate key design choices for learning across human and humanoid embodiments equipped with dexterous five-finger hands, using the $π_{0.5}$ model as a foundation. Our results show that human data enables robots to learn new task semantics and compose existing skills into novel behaviors without corresponding robot data. The paper website is here: https://egopipaper.github.io/

翻译：机器人领域面临数据稀缺的根本性挑战。与语言或视觉研究不同，机器人操作缺乏互联网规模的数据集。一个前景广阔的路径是利用以自我为中心的人类数据——这类数据更易收集、覆盖面更广、规模更大。为此，我们以$π_{0.5}$模型为基础，研究了跨人类与配备灵巧五指手的类人实体进行学习的关键设计选择。结果表明，人类数据使机器人能够学习新任务语义，并在无需对应机器人数据的情况下，将现有技能组合成新颖行为。论文网站参见：https://egopipaper.github.io/