Robots under autonomous operation may require decisions based on evidence that is no longer visible. We study delayed-evidence tasks, where an early cue disappears before a later decision point, so visually similar observations can require different actions. In these settings, the current observation is not a sufficient state for control. We introduce TRAjectory-routed Causal Evidence (TRACE), a memory framework for visuomotor imitation policies. TRACE stores task-relevant visual and robot-state evidence, such as object identity, target choice, or route-dependent state, in a fixed-size latent memory that remains bounded over long episodes. Instead of indexing memory by raw time or manually provided task labels, TRACE uses path signatures: compact, order-sensitive features of the executed robot-state trajectory. These signatures do not store the visual cue itself; rather, they provide trajectory-conditioned keys for writing and retrieving the evidence stored when the cue was visible. When the robot later reaches an ambiguous observation, the policy conditions on TRACE memory to recover the missing context and choose the correct branch. TRACE attaches through lightweight adapters to policies, without changing the policy backbone, action head, or imitation objective. Across real-world long-horizon manipulation tasks with visually ambiguous branch points, TRACE improves branch selection and task success over alternative baselines, including short-history and recurrent memory. Project page: https://jeong-zju.github.io/trace

翻译：自主运行的机器人可能需要基于不再可见的证据做出决策。我们研究延迟证据任务，其中早期线索在后续决策点之前消失，导致视觉上相似的观测结果可能需要不同的动作。在这些场景中，当前观测值并非控制目标的充分状态。我们提出了一种面向视觉运动模仿策略的记忆框架——轨迹路由因果证据（TRACE）。TRACE将任务相关的视觉和机器人状态证据（如物体身份、目标选择或路径依赖状态）存储于固定大小的潜在记忆中，该记忆在长时序任务中保持边界可控。与通过原始时间戳或手动任务标签索引记忆不同，TRACE使用路径签名：一种紧凑的、对顺序敏感的机器人执行状态轨迹特征。这些签名不存储视觉线索本身，而是提供基于轨迹条件的键，用于写入和检索线索可见时存储的证据。当机器人后续遇到模糊观测时，策略以TRACE记忆为条件，恢复缺失的上下文并选择正确分支。TRACE通过轻量级适配器附加到策略上，无需改变策略主干、动作头或模仿目标。在具有视觉模糊分支点的实际长时域操作任务中，TRACE在分支选择和任务成功率上均优于包括短历史记忆和循环记忆在内的替代基线。项目页面：https://jeong-zju.github.io/trace