Logic-VLA: A Temporal Logic Conditioned Vision-Language-Action Model
This work provides a method for VLA models to incorporate formal safety and spatiotemporal requirements, which is crucial for deploying autonomous systems in safety-critical applications.
This paper addresses the limitation of Vision-Language-Action (VLA) models in precisely handling safety-critical or spatiotemporal requirements. The authors introduce Logic-VLA, which conditions on Signal Temporal Logic (STL) specifications at inference time, improving STL satisfaction rate by 24.8 to 40.7 percentage points while maintaining nominal NL task success within 1.8 percentage points.
Vision-language-action (VLA) models can follow natural-language (NL) task instructions, but such instructions may not precisely specify safety-critical or spatiotemporal requirements on the resulting behavior. We introduce Logic-VLA, a formal-requirement-aware VLA that conditions on Signal Temporal Logic (STL) specifications supplied at inference time. Logic-VLA uses a syntax-graph-based STL encoder pre-trained to capture temporal logic semantics. Policy adaptation proceeds in two stages: STL-conditioned supervised fine-tuning on satisfying demonstrations is followed by trajectory-level preference optimization over matched satisfying-violating rollout pairs using a flow-matching surrogate for Identity Preference Optimization. This formulation improves formal requirement satisfaction while preserving the nominal NL task. We evaluate Logic-VLA in closed-loop quadcopter navigation simulation across randomized photorealistic environments and test generalization to STL formulas unseen during training. Across the evaluation benchmarks, Logic-VLA improves STL satisfaction rate over an STL-blind base policy by 24.8 to 40.7 percentage points (pp) while reducing nominal NL task success by at most 1.8 pp, showing that a single VLA can adapt its behavior to varying formal requirements without requiring a separate policy for each specification.