視覚言語行動モデルの失敗要因を特定する因子分解リスクモデル
What Would Removing This Corruption Change? Failure Attribution for Vision-Language-Action Models
VLAモデルが視覚的な破損（corruption）に敏感な点に着目し、どの破損を除去すれば失敗リスクが下がるかを推定する「失敗帰属」問題を定義。シーンと破損を分離表現する因子分解リスクモデルで、対になるクリーン画像や追加ロールアウトなしに除去効果を見積もる手法を提案した。
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: Carson Sobolewski, Anjali Parashar, Fei Chen, Yingke Li, Chuchu Fan
分類: cs.RO, cs.LG
原文アブストラクト
Vision-language-action (VLA) models are sensitive to visual corruptions, but knowing that a corruption is typically harmful does not establish whether removing it would benefit the current episode. Furthermore, when several corruptions co-occur, deciding what to correct requires understanding which ones contribute to failure risk and how much their removal would help. We define this problem as failure attribution. Detecting corruption presence alone cannot answer this question, as corruptions can be present but relatively harmless, while directly measuring removal benefits requires costly paired policy rollouts with and without the corruption present. To address this, we introduce a factorized risk model that learns separate representations of the scene and corruptions, allowing us to estimate removal benefits through latent intervention from the current image and task instruction, without paired clean images or additional counterfactual rollouts. We evaluate our approach on OpenVLA and $π_{0.5}$ in simulation and $π_{0.5}$-DROID on a Franka robot. Our method outperforms monolithic and vision-language-model (VLM) baselines, including achieving approximately 3x lower attribution error than frontier VLMs. We also demonstrate our method adapts to execution context and corruption intensity, and can be used for online failure monitoring on hardware.
