接触を伴うタスクにおけるVLAの失敗原因の解明とその修正方法
Demystifying When and Why VLAs Fail in Contact-Rich Tasks and How to Fix Them
視覚言語行動モデルが接触を伴う操作タスクで失敗する原因を2つのモードに分類し、それぞれに特化した対策を組み合わせたFACTを提案。実機2500回の評価で成功率66%を達成した。
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？
※ 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: Carlota Parés-Morlans, Nils Kuhn, Isabel Liu, Alberta Longhini, Jeannette Bohg
分類: cs.RO, cs.AI
原文アブストラクト
We address the problem of understanding when and why Vision-Language-Action models struggle with contact-rich manipulation tasks that require precise physical interaction. Prior work has primarily focused on addressing contact failures through force-augmented architectures and training-time regularizers, yet the root causes of these failures remain underexplored. We identify two distinct failure modes underlying this gap. Precision failures are rooted in a flow-matching policy training mismatch, and force failures arise from the distinctive structure of force signals. We address each failure mode with a targeted mechanism and combine them into FACT, which achieves 66% average success rate across five contact-rich tasks against 41% for the best prior baseline, in an evaluation spanning almost 2,500 real-world rollouts.
