PoSafeNet: Structured Safety Learning via Compositional Projection

Safe robot learning often involves multiple heterogeneous safety constraints that cannot always be satisfied simultaneously. Existing neural safety layers typically treat multi-constraint safety as a numerical optimization problem, enforcing all constraints through a single QP-based projection or relaxing conflicts with slack variables. This hides the semantic question of which constraints may be sacrificed under conflict inside solver geometry, penalty weights, or a fixed total hierarchy. We propose PoSafeNet, a poset-structured composable safety layer that makes these conflict semantics explicit. PoSafeNet encodes admissible safety override relations as a partial order and realizes each admissible execution by composing closed-form projections onto CBF-induced halfspaces. Across multi-obstacle navigation, constrained manipulation, and vision-based autonomous driving, PoSafeNet improves operational feasibility, computational efficiency, and task performance over dQP-based, slack-based, and hierarchical safety layers.