T²Mem: ロボティクスのためのテスト時メモリ学習
T$^2$Mem: Learning Test-Time Memory for Robotics
事前学習済み視覚言語行動ポリシーに、テスト時訓練で観測履歴を圧縮した高速重みとして記憶させ、記憶不要なベース方策を大きく上回る成功率を達成した手法。
詳しい要約
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著者: Yize Liu, Huang Huang, Yining Hong, Zijian Du, Zhi Cao, Li Fei-Fei, Jiajun Wu
分類: cs.RO
原文アブストラクト
Memory-dependent robotic manipulation requires policies to use information that is no longer available in the current observation. Retaining history alone is insufficient: memory must preserve information that supports future actions. One challenge is whether a memory-free foundation model can learn to retain and use historical information from action demonstrations alone, without external memory support. We introduce T$^2$Mem, a framework that develops this capability within a pretrained vision-language-action policy, without external reasoning models or memory-specific annotations. T$^2$Mem uses test-time training to encode observation history into compact fast weights through online self-supervised updates, avoiding repeated processing of the full history. An observation-grounded interface extracts vision-language information for memory formation and supplies retrieved context to the action expert. Action supervision shapes what the memory learns to retain and use, while alternating memory-policy learning gives each component a fixed counterpart during optimization. Across 16 RoboMME tasks, T$^2$Mem improves average success from 17.93% to 56.83% over the memory-free base policy and outperforms the recurrent-memory methods reported in the benchmark, while controlled profiling indicates at least 3x inference speedup over explicit methods. Project website: https://yzliu84.github.io/T2MEM-project/
