Feedback World Model Enables Precise Guidance of Diffusion Policy
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. We observe that execution itself provides a natural but underutilized signal: after each action, the robot directly observes the true next state, revealing the mismatch between predicted and actual outcomes. Building on this insight, we propose feedback world model, a new paradigm that closes the loop between prediction and observation at inference time. Instead of treating the world model as a static open-loop predictor, ...
| Authors | Tuo An, Jindou Jia, Gen Li et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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