让机器人学会从一次尝试中记住操作步骤，避免重复探索同一物品。
Try Once, Then Optimal: De-Redundified Procedure Memory for Cross-Episode Exploration Amortization
- 以物体为中心记录操作流程，用视觉特征快速匹配并复用。
- 真实机器人实验中减少16%-30%的操作次数，成功率不降反升。
- 无需额外训练，直接用现成视觉语言模型提取操作步骤。
操控具有隐藏状态的物体（如带锁的微波炉）需要机器人先探测才能行动。但现有跨回合记忆仅关注任务成功，按状态组织复用，导致机器人每次遇到相同实例都重复探测。本文提出实例导向记忆（IOM），一种以物体为中心的框架：从一次探测中无论成败，记录该物体的简短操作流程，基于其可识别特征存储，并作为软偏差注入条件化策略。后续遇到该物体时，系统可识别并调用已有流程，无需重新探索。我们使用现成的视觉语言模型（VLM）在不进行任务特训的情况下解析每次交互生成操作流程。在四个任务中（模拟：微波炉、门；真实机器人：瓶子、柜子），理想记忆可将操作次数减少16%-30%，而VLM实现恢复了其中69%-88%的节省效果。由于流程是反馈驱动策略上的软偏差，即使召回错误流程，系统仍能通过实时反馈修正，对约12%的门类实例依然保持成功。整体提升仅为效率，成功率未下降，真实机器人上甚至略有提高。代码将在接受后发布。
原文摘要 · Abstract (English)
Manipulating objects with hidden internal state, such as a latched microwave, forces a robot to probe before it can act. Yet a robot that has solved an instance once re-runs the same probes whenever it encounters that instance again, because existing cross-episode memories target task success and organize reuse around states, not the object or the cost of re-exploring it. We present Instance-Oriented Memory (IOM), an object-centric framework that amortizes this exploration: from a single encounter that uncovers the hidden state, whether or not it succeeds, IOM records a short procedure for manipulating that instance, keys it on the object's identifiable features, and injects it as a soft bias on a procedure-conditioned policy. A later encounter recognizes the object and recalls its procedure instead of re-exploring. We instantiate this distillation with an off-the-shelf vision-language model (VLM) that parses each encounter into the procedure without task-specific training. Across four articulated-object tasks, two in simulation (microwave, door) and two on a real robot (bottle, cabinet), an oracle procedure memory cuts manipulation operations by 16-30% over re-exploration at non-regressing success, and the VLM instantiation recovers 69-88% of that saving out of the box. Because the procedure is a soft bias on a feedback-driven policy, an incorrect memory is recovered from rather than obeyed: success holds even when a retrieved procedure is wrong, as for $\approx$12% of door instances. Across all tasks the benefit is purely one of efficiency: success never regresses, and on the real robot even improves. Code will be released upon acceptance.
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