Signal Temporal Logic

Signal Temporal Logic (STL) is a formal language for specifying complex temporal properties of continuous signals, primarily used to define and verify the behavior of dynamic systems. Current research focuses on integrating STL with optimization-based planning and control algorithms, including model predictive control and reinforcement learning, often employing neural networks to enhance scalability and robustness. This work is significant for enabling the design and verification of safe and reliable autonomous systems across diverse applications, such as robotics, autonomous vehicles, and multi-agent systems, by providing a rigorous framework for specifying and ensuring desired behaviors.