ReCAT: 記憶・計数・時間を考慮した構造化リカレントメモリによるロボットマニピュレーション
ReCAT: Remember, Count, and Time: Structured Recurrent Memory for Robot Manipulation
言語条件付きポリシーにMamba-2と因果注意層による構造化リカレントメモリを組み込み、過去の視覚手がかりの想起やイベント計数、経過時間推定を必要とするロボット操作タスクを高精度で実現した。
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？
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著者: Pankhuri Vanjani, Mostafa Hatab, Can Mizrakli, Vaisakh Shaj, Zhuoyue Li, Moritz Reuss, Rudolf Lioutikov
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
Memory-dependent manipulation requires robots to make decisions using information that is no longer available to their current sensors, such as recalling an earlier visual cue, tracking task progress, counting repeated events, or estimating elapsed time. We present ReCAT, a language-conditioned policy with structured recurrent memory. An instruction-conditioned encoder forms features from the current observation. A recurrent memory integrates the observation stream through Mamba-2 layers and one causal attention layer. A flow-matching Transformer decoder reads the current and the historical representation through separate cross-attention in every block. ReCAT reaches 95.3\% average success on LIBERO and 62.4\% on RMBench, with the best or tied-best result on six of nine tasks. On three real-robot tasks probing spatial recall, event counting, and interval timing, the best ReCAT variant reaches 66.7\% average success, against 8.3\% for the strongest short-history baseline. Controlled comparisons within ReCAT show that the observation encoder and every-block memory conditioning are needed for this performance. They also show that update rules developed for efficient sequence modeling behave differently as robot memory: additive updates have the highest observed success on counting and timing, and delta-rule updates on spatial recall. Project website is at https://intuitive-robots.github.io/ReCAT