記憶に値するもの：驚きでゲートされたロボットのエピソード記憶
Worth Remembering: Surprise-Gated Robot Episodic Memory
ロボットの長期記憶を選択的に保持するため、ベイズ的驚きをゲート機構として用いる手法を提案。V-JEPA-2の潜在空間で驚きを計算し、4Dシーングラフ空間記憶と組み合わせることで、ロボットの質問応答性能を向上させた。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Nicolas Gorlo, Derek K. Wise, Alberto Speranzon, Luca Carlone
分類: cs.RO
原文アブストラクト
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.
