Author

Bing-Jia Huang

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Preprint Sep 2026

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

Zeva-Ego is introduced, a unified framework that learns physical priors from human experience and evolves through robot interaction and demonstrates a scalable path toward embodied intelligence that learns from human experience and continuously improves through its own interaction.

Bing-Jia Huang, Xin Ding, Fu Chen et al. · 0 citations

Preprint Aug 2026

Zeva: In-Context Causal Learning for Generalizable Embodied Manipulation

Zeva is presented, the first framework that enables in-context learning from a robot's own physical interaction experience while keeping the policy model frozen, and achieves the best performance among the compared frontier VLAs and WAMs and enables self-evolution during deployment without gradient updates.

Fu Chen, Xin Ding, Bing-Jia Huang et al. · 3 citations