# CASIA and others propose World4RL: diffusion world model combined with reinforcement learning to optimize robot manipulation policies, real-world success rate rises to 93.3%

- Source: 中科慧灵 · 微信公众号
- Published: 2026-09-04T10:00:00.000Z
- RoboHot: https://robohot.news/en/items/ygoho3u2ai48inv8mqc4qjm8o
- Original: https://mp.weixin.qq.com/s?__biz=MzkxMTY3MTQzNQ==&mid=2247487489&idx=1&sn=357fd68a0f69a89ae7ba291194824dbd&chksm=c0a6b0c8f853ae0234cfad8db465f649bc0d8c992f71edacfaca8e6cf4bad6ebb648da30d94a&scene=126&sessionid=1792593834#rd

## Summary

The Institute of Automation of the Chinese Academy of Sciences, Zhongguancun Academy, and the CASBOT team propose World4RL, a policy optimization framework for robot manipulation that combines a diffusion world model with reinforcement learning, enabling robot policies to be optimized end-to-end in imagined trajectories generated by the model without additional real-robot interaction.

## Why it matters

The paper turns the world model from a trajectory-prediction tool used at test time into a reinforcement learning training environment, giving readers a concrete path to understanding how the cost of real-robot interaction can be reduced.