凍結VLAポリシーのための達成基盤メモリによる閉ループエージェント
AGM: Achievement-Grounded Memory for Closed-Loop Agents with Frozen VLA Policies
凍結された視覚-言語-行動(VLA)ポリシーに、物理的証拠で検証されたサブゴール進行ポインタを持つ軽量メモリを追加し、開ループ実行を閉ループ化するフレームワークを提案。
詳しい要約
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著者: Hongbo Gao, Zeyu Ni, Xin Wen, Siyu Xu, Ruifeng Li
分類: cs.RO, cs.AI
原文アブストラクト
Frozen vision-language-action (VLA) policies offer broad manipulation skills but execute open-loop action chunks without tracking task progress, so the agent cannot reliably decide whether to continue, retry, or terminate. External memory is a natural remedy, yet it can be harmful when attempted actions are treated as completed progress, turning local execution errors into persistent task-state errors. We propose Achievement-Grounded Memory (AGM), a lightweight closed-loop framework for frozen VLA policies that represents a task as a subgoal sequence with a progress pointer and advances this memory only after the current subgoal is verified by physical evidence. Proprioceptive interaction cues decide when to verify, while coherent point tracking and language-conditioned cross-view comparison, sourced from frozen foundation models through a single 2.43M-parameter verification head, decide what was achieved. AGM thereby converts open-loop execution into a closed loop of execution, verification, and progress, keeping the policy frozen without test-time large-model inference. On the RoboMME Counting benchmark, AGM reaches on PickXTimes and on BinFill, surpassing the strongest memory-augmented baseline by points on average, and the framework yields equally decisive gains on a physical robot. Reliable embodied memory thus depends more on disciplined state updates than on memory capacity.
