用意外度筛选记忆，让机器人记住有用经历
Worth Remembering: Surprise-Gated Robot Episodic Memory
- 用贝叶斯意外度决定存哪些记忆，自动选高价值事件
- 在时空问答任务上比现有方法提升至少12%
- 无需标注也能分清事件边界，适合通用机器人
解决通用任务的机器人需将指令与过往经验关联，因为人类常提及重要往事（如‘带我去昨天化学品泄漏的地方’）。由于记忆容量有限，长期记忆必须有选择地保存。但通用机器人无法预知未来任务，因此我们提出以贝叶斯意外度作为记忆形成的门控机制。基于V-JEPA-2提供的语义丰富、部署无关的潜在空间计算意外度，结合4D场景图的空间记忆，所提方法在机器人问答任务中持续领先：对时间、空间和二元问题，性能优于现有方法至少12%；在事件分割任务中，以无监督因果方法超越监督及非因果方法。
原文摘要 · Abstract (English)
Robots solving generalist tasks need to be able to ground instructions in their past experience, since humans may refer to notable past events when giving a task (e.g., ``Take me to where the chemical spill happened yesterday''). Since memory limits make storing all past events infeasible, long-term robot memory must be selective, ideally retaining only those episodes with high utility for future tasks. However, future tasks are not typically given a priori for generalist robots. To select generically useful memories, we propose Bayesian surprise as a gating mechanism for memory formation. We present an approach to compute surprise in a semantically rich deployment-agnostic latent space provided by V-JEPA-2. Using our gated episodic memory to augment 4D scene graph-based spatial memory, we show a consistent improvement over state-of-the-art benchmarks in robot question answering, outperforming prior robot memory methods by $\geq12\%$ for temporal, spatial, and binary questions, and surpassing the performance of supervised and non-causal methods with an unsupervised causal method in event segmentation tasks.
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