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arXiv · 2606.08107

Ego-Pi: VLA Fine-Tuning for Ego-Centric Human and Robot Data

Ji Woong Kim·Ke Wang·Zipeng Fu·Sirui Chen·Cong Zhao·Jeff Lai·Chelsea Finn
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Abstract

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 $\pi_{0.5}$ 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/

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Ji Woong Kim, Ke Wang, Zipeng Fu, Sirui Chen, Cong Zhao, Jeff Lai, Chelsea Finn. 2026-06-06. Ego-Pi: VLA Fine-Tuning for Ego-Centric Human and Robot Data. https://arxiv.org/abs/2606.08107

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