用偏序结构设计安全神经层，让机器人更灵活地处理复杂安全约束。
PoSafeNet: Safe Learning with Poset-Structured Neural Nets
- 将安全约束建模为偏序集，支持部分可比较的多约束场景
- 通过可微分闭式投影实现安全执行路径的自适应选择
- 在导航、操控和自动驾驶中验证了更强的可行性与鲁棒性
安全学习对部署于关键任务机器人的学习型控制器至关重要，但现有方法通常以统一或固定优先级方式施加多个安全约束，导致不可行或行为脆弱。实际上，安全需求具有异质性，仅部分约束之间存在可比关系。本文将此设定形式化为偏序结构安全，将安全约束建模为偏序集，并将安全组合视为策略类的结构性属性。基于该框架，提出PoSafeNet——一种可微分神经安全层，通过偏序一致的约束顺序进行序列闭式投影，实现有效安全执行路径的自适应选择或混合，且天然保持优先级语义。在多障碍物导航、受约束机器人操作及视觉自主驾驶任务上的实验表明，该方法在可行性、鲁棒性和可扩展性方面优于无结构及基于可微分二次规划的安全层。
原文摘要 · Abstract (English)
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.
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