Zeva-Ego: Egocentric Mid-Training with In-Context Causal Learning for Robot Manipulation

Abstract:Egocentric video offers a scalable source of physical interaction experience, yet translating it into robot-executable knowledge and enabling continual adaptation remain challenging. We introduce Zeva-Ego, a unified framework that learns physical priors from human experience and evolves through robot interaction. An Action-Centric Encoder (ACE) converts egocentric visual transitions into action-centered supervision for VLA mid-training, while In-Context Causal Learning (ICCL) enables parameter-free adaptation from action-effect feedback at deployment. Scaling Ego data to 10K hours improves RoboTwin success from 63.8% to 75.3%, matching 2K hours of robot demonstrations (74.7%), corresponding to an empirical data ratio of roughly 4-5:1. With accumulated interaction experience, ICCL further improves success from 58% to 89% within four attempts without parameter updates. These results demonstrate a scalable path toward embodied intelligence that learns from human experience and continuously improves through its own interaction.

Robotics (cs.RO)

arXiv:2609.24411 [cs.RO]

(or arXiv:2609.24411v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.24411 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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