Khang Vo Huynh

Chengyue Huang, Khang Vo Huynh, Sebastian Elbaum et al.

Robotic manipulation is typically evaluated by task success, but successful completion does not guarantee safe execution. Many safety failures are temporal: a robot may touch a clean surface after contamination or release an object before it is fully inside an enclosure. We introduce SafeManip, a property-driven benchmark to explicitly evaluate temporal safety properties in robotic manipulation, moving beyond prior evaluations that largely focus on task completion or per-state constraint violations. SafeManip defines reusable safety templates over finite executions using Linear Temporal Logic over finite traces (LTLf). It maps observed rollouts to symbolic predicate traces and evaluates them with LTLf-based monitors. Its property suite covers eight manipulation safety categories: collision and contact safety, grasp stability, release stability, cross-contamination, action onset, mechanism recovery, object containment, and enclosure access. Templates can be instantiated with task-specific objects, fixtures, regions, or skills, allowing the same safety specifications to generalize across tasks and environments. We evaluate SafeManip on six vision-language-action policies, including $π_0$, $π_{0.5}$, GR00T, and their training variants, across 50 RoboCasa365 household tasks. Results show that even strong models often behave unsafely. Task-success gains do not reliably translate into safer execution: many successful rollouts remain unsafe, while longer-horizon or more complex tasks expose more violations. SafeManip provides a reusable evaluation layer for diagnosing temporal safety failures and measuring safe success beyond task completion.

Khang Vo Huynh, David Parker, Lu Feng

We present an optimization-based framework for robust permissive synthesis for Interval Markov Decision Processes (IMDPs), motivated by robotic decision-making under transition uncertainty. In many robotic systems, model inaccuracies and sensing noise lead to interval-valued transition probabilities. While robust IMDP synthesis typically yields a single policy and permissive synthesis assumes exact models, we show that robust permissive synthesis under interval uncertainty can be cast as a global mixed-integer linear program (MILP) that directly encodes robust Bellman constraints. The formulation maximizes a quantitative permissiveness metric (the number of enabled state-action pairs), while guaranteeing that every compliant strategy satisfies probabilistic reachability or expected reward specifications under all admissible transition realizations. To address the exponential complexity of vertex-based uncertainty representations, we derive a dualization-based encoding that eliminates explicit vertex enumeration and scales linearly with the number of successors. Experimental evaluation on four representative robotic benchmark domains demonstrates scalability to IMDPs with hundreds of thousands of states. The proposed framework provides a practical and general foundation for uncertainty-aware, flexibility-preserving controller synthesis in robotic systems.