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原文摘要

In long-horizon manipulation, a robot may need to take different actions under identical observations, depending on task progress and the outcomes of earlier attempts. We refer to this ambiguity as decision aliasing. Resolving it requires the policy to maintain memory of its execution history. To reduce the cost of processing full observation histories, recent methods build compact event memories using perceptual, semantic, or kinematic cues. However, these criteria may miss events that matter for task success. We instead propose an outcome-grounded principle: remember events that substantially change the predicted probability of eventual success. Following this principle, we introduce CGEM, a critic-gated event memory that uses a critic to select events with large estimated changes in success probability. Positive and negative changes are encoded as progress and failure records, respectively. Failure records are discarded once the corresponding subtask is completed. In a ten-subtask diagnostic environment with exactly computed success probabilities, CGEM achieves 88.0% success, compared with 0.0% for both a 36-step observation window and a kinematic event memory. Removing failure records reduces success to 1.7%.