PoSafeNet: 半順序構造ニューラルネットによる安全学習
PoSafeNet: Safe Learning with Poset-Structured Neural Nets
安全制約を半順序集合として扱い、優先順序を保ちながら逐次射影で安全を保証する微分可能なニューラル安全層を提案。
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？
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著者: Kiwan Wong, Wei Xiao, Daniela Rus
分類: cs.LG, cs.RO
原文アブストラクト
Safe learning is essential for deploying learningbased controllers in safety-critical robotic systems, yet existing approaches often enforce multiple safety constraints uniformly or via fixed priority orders, leading to infeasibility and brittle behavior. In practice, safety requirements are heterogeneous and admit only partial priority relations, where some constraints are comparable while others are inherently incomparable. We formalize this setting as poset-structured safety, modeling safety constraints as a partially ordered set and treating safety composition as a structural property of the policy class. Building on this formulation, we propose PoSafeNet, a differentiable neural safety layer that enforces safety via sequential closed-form projection under poset-consistent constraint orderings, enabling adaptive selection or mixing of valid safety executions while preserving priority semantics by construction. Experiments on multi-obstacle navigation, constrained robot manipulation, and vision-based autonomous driving demonstrate improved feasibility, robustness, and scalability over unstructured and differentiable quadratic program-based safety layers.
