Khang Vo Huynh | Searcharxiv

arXiv2026

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.

cs.RO↗

arXiv2026

Optimization-Based Robust Permissive Synthesis for Interval MDPs

We present an optimization-based framework for robust permissive synthesis for Interval Markov Decision Processes (IMDPs). While robust IMDP controller synthesis typically yields a single policy and most permissive-synthesis methods assume exact transition models, we synthesize multi-strategies that retain multiple actions while guaranteeing satisfaction of probabilistic reachability or expected-reward specifications under all admissible transition probabilities. We formulate the problem as a mixed-integer linear program (MILP) that maximizes the number of enabled state--action pairs subject to robust Bellman constraints. We develop two encodings: a direct vertex-enumeration formulation and a dualization-based formulation that avoids explicit enumeration of uncertainty-polytope vertices and has size linear in the number of successor transitions. Experiments on four benchmark domains show that both encodings achieve the same optimal permissiveness and scale to IMDPs with hundreds of thousands of states. Compared with standard robust single-policy synthesis, the resulting multi-strategies retain substantially more action choices.

cs.RO↗