Zero-Shot Signal Temporal Logic Planning with Disjunctive Branch Selection in Dynamic Semantic Maps
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Abstract (arXiv)
Signal Temporal Logic (STL) offers verifiable task specifications and is crucial for safety-critical control. Yet STL planning remains challenging: exact optimization-based methods are often too slow, and learning-based methods struggle to generalize across varying environments. We propose a zero-shot STL planning solver for variable-map environments that generates feasible trajectories without retraining. By integrating a map-conditioned Transformer architecture with a lightweight heuristic, our approach effectively handles complex disjunctive (OR) subformulas. Furthermore, we leverage Transitive Reinforcement Learning (TRL) to ensure consistent temporal grounding and logical coherence across decomposed sub-tasks. Experiments on dynamic semantic maps with diverse obstacle layouts demonstrate consistent gains, highlighting the framework's superior zero-shot generalization to changing environments and broad STL coverage.
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References · 18
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- 2022Mixed-Integer Programming for Signal Temporal Logic with Fewer Binary Variablescited by 15
- 2023Signal Temporal Logic Neural Predictive Controlcited by 12
6 more
- 2025Transitive RL: Value Learning via Divide and Conquercited by 12
- 2021Model-Free Reinforcement Learning for Optimal Control of MarkovDecision Processes Under Signal Temporal Logic Specificationscited by 10
- 2025TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matchingcited by 9
- 2025Zero-Shot Trajectory Planning for Signal Temporal Logic Taskscited by 6
- 2025TGPO: Temporal Grounded Policy Optimization for Signal Temporal Logic Taskscited by 5
- 2023Continuous-time control synthesis under nested signal temporal logic specificationscited by 3
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