Feedback World Model Enables Precise Guidance of Diffusion Policy
The article discusses a new approach called the feedback world model that enhances robotic decision-making by integrating real-time feedback. This method addresses the limitations of traditional world models, which often fail when robots encounter unfamiliar states. Experimental results indicate significant improvements in prediction accuracy and policy performance, particularly in out-of-distribution scenarios.
- ▪The feedback world model closes the loop between prediction and observation at inference time.
- ▪It reduces world model prediction error by up to 76.4% and improves out-of-distribution success rates by 30%.
- ▪The method updates a lightweight feedback state online to correct future predictions without requiring additional training data.
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Computer Science > Robotics arXiv:2605.15705 (cs) [Submitted on 15 May 2026] Title:Feedback World Model Enables Precise Guidance of Diffusion Policy Authors:Tuo An, Jindou Jia, Gen Li, Jingliang Li, Chuhao Zhou, Pengfei Liu, Bofan Lyu, Jiaqi Bai, Xinying Guo, Geng Li, Jianfei Yang View a PDF of the paper titled Feedback World Model Enables Precise Guidance of Diffusion Policy, by Tuo An and 10 other authors View PDF HTML (experimental) Abstract:World models aim to improve robotic decision making by predicting the consequences of actions. However, in practice, their predictions often become unreliable once the robot encounters states outside the training distribution, limiting their effectiveness at deployment.
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