必要な時だけ考える:視覚言語行動操作における選択的スローパス介入のためのプロンプト権限制御
Think Only When Needed: Prompt-Authority Control for Selective Slow-Path Intervention in Vision-Language-Action Manipulation
凍結された視覚言語行動(VLA)ポリシーに検索テキストを追加すると、プロンプト形式の変化が実行を支配し成功率が大幅に低下する「プロンプト形式崩壊」を特定し、候補生成とポリシー入力変更の許可を分離するプロンプト権限インターフェースTOWN-VLAを導入。これにより、シミュレーションと実機で成功率が向上した。
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: Zhiruo Zhou, Zelin Li, Xiwen Chen, Jiazhuo Li, Chenwei Wang, Huiming Chen, Xiaojun Zhu
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
Retrieval can efficiently and effectively augment a frozen vision--language--action (VLA) policy without retraining, yet retrieved text becomes a control intervention once it enters the executed prompt. In a matched audit, raw appended text reduces mean success from 92.47\% to 3.00\%, while meaningful and length-matched meaningless appends both fail on all 500 states. This result identifies \emph{prompt-form collapse}: changing the instruction form, rather than adding useful semantics, can dominate execution. We introduce TOWN-VLA (Think Only When Needed), a prompt-authority interface that separates candidate generation from permission to alter the policy input. A fixed compatibility rule authorizes a canonical compact instruction; otherwise, the interface restores the original Base prompt exactly. Across 900 audited routes, every route follows this contract: 525 routes recover Base with matching hashes, and all 375 authorized prompts preserve the task signature. On a matched $4\times7$ LIBERO-Plus evaluation with 10{,}030 episodes per method, success rises from 69.5\% to 73.1\% ($+362$ episodes; 95\% CI 1.89--5.45 points), improving on six perturbation axes and all four suites. On a physical PiPER arm with a frozen \pizerofive{} checkpoint, success rises from 52.7\% to 78.7\% over 150 trials per method ($p=3.16\times10^{-6}$). Prompt authority is enforceable for a frozen controller; oracle-free admission calibration is the next deployment target.