行動・思考・棄権：VLAモデルのための複雑度適応型推論
Act, Think or Abstain: Complexity-Aware Adaptive Inference for Vision-Language-Action Models
視覚言語行動モデルの推論を状態の複雑さに応じて「即実行・熟考・停止」に動的に振り分ける適応フレームワークを提案し、計算効率と異常検知性能を両立させた。
詳しい要約
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2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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著者: Riccardo Andrea Izzo, Gianluca Bardaro, Matteo Matteucci
分類: cs.CV, cs.RO
原文アブストラクト
Current research on Vision-Language-Action (VLA) models predominantly focuses on enhancing generalization through reasoning techniques. While effective, these improvements increase computational complexity and inference latency. Furthermore, these mechanisms are typically applied indiscriminately, wasting resources on trivial tasks while failing to provide the uncertainty estimation necessary to prevent catastrophic failure on out-of-distribution scenarios. Inspired by human cognition, we propose an adaptive framework that dynamically routes VLA execution based on the complexity of the perceived state. Our approach transforms the VLA's vision-language backbone into an active detection tool by projecting latent embeddings into a set of parametric and non-parametric estimators. This allows the system to execute known tasks immediately (Act), reason about ambiguous scenarios (Think), and preemptively halt execution when encountering physical or semantic anomalies (Abstain). We find that a Gaussian Mixture Model fitted to fused vision-language embeddings provides the most reliable task-complexity signal, combining visual novelty with instruction context and cross-modal compatibility. Evaluated on the LIBERO and LIBERO-PRO benchmarks as well as on a real robot, our fused configuration achieves up to 87.5% F1-score across two VLA backbones (SmolVLA and $\pi_0$), retains 83% with as little as 5% of training data, and surpasses state-of-the-art failure detectors.
