Zeva: In-Context Causal Learning for Generalizable Embodied Manipulation

2026-08-31

AI Summary

为解决机器人在真实世界中因未见物理条件导致的操作泛化难题，Zeva通过实时学习自身物理交互经验并将其作为上下文注入冻结的策略模型来指导后续动作。

Abstract

Generalizable embodied manipulation remains difficult to achieve through pretraining alone, due to unseen physical conditions in the real world. We argue that robots need to learn from their own physical interactions on the fly during real-world deployment and use this knowledge to inform subsequent actions. We present Zeva, the first framework that enables in-context learning from a robot's own physical interaction experience while keeping the policy model frozen. Zeva employs a Causal Interaction Extractor to encode an executed action and its induced state change into a causal interaction signal, which is stored in a dual-timescale causal memory. For subsequent actions, relevant causal interaction signals are retrieved from memory and injected into the frozen policy model as context. Experiments in simulation and real-world manipulation demonstrate that Zeva achieves the best performance among the compared frontier VLAs and WAMs and, more importantly, enables self-evolution during deployment without gradient updates. Its success rate continues to improve as the robot accumulates interaction experience. Furthermore, the acquired interaction experience can generalize across tasks.

Problem

• Generalizable Embodied Manipulation
• Physical Interactions
• In-Context Learning

Innovation

• in-context learning
• causal interaction extractor
• dual-timescale causal memory
• frozen policy model