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About this paper
Tuo An, Jindou Jia, Gen Li, Jingliang Li, Chuhao Zhou, Pengfei Liu, Bofan Lyu, Jiaqi Bai and 3 more
The authors introduce a feedback world model that corrects a robot’s future predictions online from the mismatch between predicted and observed states, without deployment-time data collection or parameter updates. Action-aware latent weighting emphasizes features responsive to candidate actions. On real-world Drawer-Open, one-chunk-ahead prediction MSE fell 76.4%; this is a task-specific prediction result, not task success.
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