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BEACON: Belief-Enabled Adaptive CONtrol for Imitation Learning under Uncertainty

arXiv · AI, language, vision and robotics · article · Sep 19, 2026 · UTC

Robot manipulation tasks often involve hidden state information that cannot be directly observed and must be inferred through sequential physical interactions. In such partially observable settings, conditioning an imitation learning policy directly on the recent raw observation history leads to poor performance. This is due to state aliasing, wherein identical observations may arise from different hidden states, and the policy receives conflicting action labels for the same input. To enable history-aware disambiguation capability, we propose conditioning a diffusion policy on a structured rep

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