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
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
Zetta is presented, a closed-loop embodied harness that evolves code-based runtime critics and recovery skills online while keeping the base policy frozen, and shows that closed-loop harness self-evolution opens a scaling path for reliable physical intelligence.