SafeManip: A Property-Driven Benchmark for Temporal Safety Evaluation in Robotic Manipulation

Robotic manipulation is typically evaluated by task success, but reaching the correct final state does not guarantee safe execution. A robot may succeed despite contamination, premature release, or incorrect sequencing. These failures are poorly captured by task-completion metrics or isolated state checks because safety often depends on how behavior unfolds over time. We introduce SafeManip, a benchmark for evaluating temporal safety directly from low-level manipulation rollouts across eight physical and semantic categories. SafeManip grounds symbolic manipulation predicates into reusable temporal properties over finite executions, formalized with LTLf. We further introduce ManipVerse, a property ontology enabling semantic safety rules generalize across objects, tasks, and environments. Finally, SafeManip introduces per-trigger safety metrics that normalize violations by the safety-relevant opportunities that activate each obligation, accounting for differences in how often those opportunities arise across policies and tasks. We instantiate SafeManip on RoboCasa365 and LIBERO to audit seven state-of-the-art robot foundation-model policies. Our results reveal a gap between task capability and temporal safety: higher task success does not necessarily imply safer execution, successful rollouts can still contain violations, and failure patterns vary systematically across safety categories, task horizons, and manipulation suites. SafeManip provides a reusable layer for evaluating how safely manipulation tasks are executed, not only whether they are completed.