VLAは呼び出すエージェントから何を学べるか：具現化ナビゲーションのためのタスクレベル経験蒸留
What Can a VLA Learn from the Agent That Calls It? Task-Level Experience Distillation for Embodied Navigation
LLMプランナー付きナビゲーションエージェントの成功経験をVLAに蒸留し、テスト時はVLA単体でナビゲーションできるようにするH2E手法を提案。REVERIE-CEで成功率を35.0%から44.9%に向上させた。
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: Jinze Tao, Yuxiao Han, Kangkang Dong, Yancong Wei, Xiaojun Zhu, Bokui Chen, Houde Liu
分類: cs.RO, cs.LG
原文アブストラクト
Navigation agents use a large language model (LLM) planner to call a vision-language-action (VLA) model. Each episode needs the LLM, adding cost and delay. We distill successful agent experience into the VLA instead of distilling the planner. The VLA then navigates alone at test time. Agent sessions are not VLA demonstrations. They include motions driven by planner tools and sub-instructions, along with detours. We present H2E, a task-level experience distillation method that converts these sessions into VLA training data. Every motion becomes a VLA action under the original task instruction, with the resulting poses as targets. H2E masks the loss on steps that move away from the goal. It labels every visited state within 1 m of the goal as a stop. We then update the VLA with low-rank adaptation. The VLA learns to stop at the goal and find goals the base model never reaches. On all 3,189 REVERIE-CE val-unseen episodes, H2E raises the success rate of an 8B VLA from 35.0% to 44.9%.
