二値実行可能性オラクルから安全なフローマッチングロボット方策を学習する手法
Learning Safe Flow Matching Robot Policies from Binary Feasibility Oracles
関節限界や衝突回避などの制約を解析的に記述できず、二値の可否判定オラクルしか使えない状況で、フローマッチング方策にオラクル誘導の微分可能な安全機構を組み込み、学習と推論の両方で制約を満たす動作生成を実現した。
著者: Jianming Ma, Rongjun Jin, Yizhi Chen, Yue Gao
分類: cs.RO, cs.AI
原文アブストラクト
Robot foundation models increasingly rely on flow-based action generation for general-purpose manipulation, yet the generated actions are not guaranteed to satisfy physical constraints such as joint limits and collision avoidance. This problem is especially challenging when the feasible set cannot be described analytically and is accessible only through black-box feasibility oracles. We study this practical oracle-only setting and propose an oracle-guided differentiable safeguard for safe flow-matching robot policies. The safeguard uses binary feasibility queries to correct unsafe updates in the forward pass, while extracting local boundary information to provide constraint-aware learning signals in the backward pass. Combined with discrete-time flow matching, the same safeguard is used during training and inference, enabling forward--backward aligned policy learning without explicit or differentiable constraint models. Experiments on obstacle-aware robotic manipulation demonstrate 100% constraint satisfaction while preserving task performance.