VLAは失敗を予見できるか？物理AIシステムにおける解釈可能な成功・失敗パターン
Do VLAs Know They Will Fail? Interpretable Success and Failure Patterns in Physical AI Systems
OpenVLAの活性化に疎オートエンコーダを適用し、失敗前に結果を予測できる特徴を発見するとともに、成功・失敗の各段階に対応する行動パターンを解釈する手法を提案した。
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？
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著者: Christian A. Rogers, Qian Lin, Daniel S. Brown
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Vision-Language-Action (VLA) models have emerged as powerful general-purpose robot policies, but their successes and failures remain difficult to diagnose and understand. In this work, we show that sparse autoencoder (SAE) representations of OpenVLA activations contain outcome-predictive information and can be used to predict task failure before the robot reaches an unrecoverable state. Moreover, to provide insights into what common behaviors may contribute to these outcomes, we identify individual SAE features whose strong activations are associated with specific stages of success or failure, and uncover recurring behavioral patterns by localizing temporal intervals where these features exhibit significant activation changes. We further develop a VLM-based interpretation pipeline to generate behavioral descriptions of these patterns and validate their correspondence to feature activations on held-out intervals.