Neuro-Symbolic Predicate Learning for Semantic Safe Robot Control
NEUPRO proposes a neuro-symbolic framework to learn safety rules directly from visual inputs. It uses differentiable reasoning to map raw images to logical predicates, allowing safety requirements to be expressed as interpretable first-order logic. This enables the system to reason about robot safety using human-specified rules, grounding learned features in understandable semantics for safe control.