# Worth Remembering: Surprise-Gated Robot Episodic Memory

arXiv: https://arxiv.org/abs/2606.03787 · HTML: https://mlsanity.com/paper/2606.03787/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2026.

## Built on by (most cited first, 1 of 1)

- [GESTO: Human-Centric Spatio-Temporal Memory for Reasoning in Dynamic Scenes](https://mlsanity.com/paper/2608.10886/) (2026)

## Abstract (arXiv)

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 ≥12% 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.