Ego-Pi: VLA Fine-Tuning for Ego-Centric Human and Robot Data
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.githu...
| Authors | Ji Woong Kim, Ke Wang, Zipeng Fu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 6 June 2026 |
| Last updated at source | 6 June 2026 |
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