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