# Safe Planning through Incremental Decomposition of Signal Temporal Logic Specifications

arXiv: https://arxiv.org/abs/2403.10554 · HTML: https://mlsanity.com/paper/2403.10554/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2024.

## Built on by (most cited first, 6 of 6)

- [STLCG++: A Masking Approach for Differentiable Signal Temporal Logic Specification](https://mlsanity.com/paper/2501.04194/) (2025)
- [Zero-Shot Trajectory Planning for Signal Temporal Logic Tasks](https://mlsanity.com/paper/2501.13457/) (2025)
- [TGPO: Temporal Grounded Policy Optimization for Signal Temporal Logic Tasks](https://mlsanity.com/paper/2510.00225/) (2025)
- [Logically Constrained Robotics Transformers for Enhanced Perception-Action Planning](https://mlsanity.com/paper/2408.05336/) (2024)
- [STeP: Signal Temporal Logic for Precise Specifications for Action Generation with Vision Language Models](https://mlsanity.com/paper/2607.18580/) (2026)
- [Tolerance of Reinforcement Learning Controllers against Deviations in Cyber Physical Systems](https://mlsanity.com/paper/2406.17066/) (2024)

## Abstract (arXiv)

Trajectory planning is a critical process that enables autonomous systems to safely navigate complex environments. Signal temporal logic (STL) specifications are an effective way to encode complex temporally extended objectives for trajectory planning in cyber-physical systems (CPS). However, planning from these specifications using existing techniques scale exponentially with the number of nested operators and the horizon of specification. Additionally, performance is exacerbated at runtime due to limited computational budgets and compounding modeling errors. Decomposing a complex specification into smaller subtasks and incrementally planning for them can remedy these issues. In this work, we present a way to decompose STL requirements temporally to improve planning efficiency and performance. The key insight in our work is to encode all specifications as a set of reachability and invariance constraints and scheduling these constraints sequentially at runtime. Our proposed technique outperforms the state-of-the-art trajectory synthesis techniques for both linear and non linear dynamical systems.