Signal Temporal Logic

Also known as STL

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Latest papers 32

6 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Feasibility Restoration under Conflicting STL Specifications with Pareto-Optimal Refinement
Mar 6, 2026Tianhao Wu, Yiwei LyuSignal Temporal Logic Model Predictive Control
The author is with the Department of Computer Science, University of Southern California. · The author is with the Department of Computer Science and Engineering, Texas A&M University.
Distilling Formal Logic into Neural Spaces: A Kernel Alignment Approach for Signal Temporal Logic
Mar 5, 2026Sara Candussio, Gabriele Sarti, Gaia Saveri +1Neuro-Symbolic Reasoning Signal Temporal Logic
AILab, MIGe, University of Trieste, IT · Khoury College of Computing Sciences, Northeastern University, Boston, MA, USA

Mar 6, 2026J/K move · Enter open · S save

Feasibility Restoration under Conflicting STL Specifications with Pareto-Optimal Refinement

Tianhao Wu, Yiwei Lyu

The author is with the Department of Computer Science, University of Southern California. · The author is with the Department of Computer Science and Engineering, Texas A&M University.

Signal Temporal Logic Model Predictive Control

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Signal Temporal Logic (STL) is expressive formal language that specifies spatio-temporal requirements in robotics. Its quantitative robustness semantics can be easily integrated with optimization-based control frameworks. However, STL specifications may become conflicting in real-world applications, where safety rules, traffic regulations, and task objectives can be cannot be satisfied together. In these situations, traditional STL-constrained Model Predictive Control (MPC) becomes infeasible and default to conservative behaviors such as freezing, which can largely increase risks in safety-critical scenarios. In this paper, we proposes a unified two-stage framework that first restores feasibility via minimal relaxation, then refine the feasible solution by formulating it as a value-aware multi-objective optimization problem. Using

\varepsilon

-constraint method, we approximate the Pareto front of the multi-objective optimization, which allows analysis of tradeoffs among competing objectives and counterfactual analysis of alternative actions. We demonstrate that the proposed approach avoids deadlock under conflicting STL specifications and enables interpretable decision-making in safety-critical applications by conducting a case study in autonomous driving.

Latest papers 32

Feasibility Restoration under Conflicting STL Specifications with Pareto-Optimal Refinement

Distilling Formal Logic into Neural Spaces: A Kernel Alignment Approach for Signal Temporal Logic