# Event-Based Signal Temporal Logic Synthesis for Single and Multi-Robot Tasks

arXiv: https://arxiv.org/abs/2011.00370 · HTML: https://mlsanity.com/paper/2011.00370/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [LTL-Constrained Policy Optimization with Cycle Experience Replay](https://mlsanity.com/paper/2404.11578/) (2024)
- [Logic programming for deliberative robotic task planning](https://mlsanity.com/paper/2301.07550/) (2023)
- [Neural Network for Weighted Signal Temporal Logic](https://mlsanity.com/paper/2104.05435/) (2021)
- [Learning Performance Graphs from Demonstrations via Task-Based Evaluations](https://mlsanity.com/paper/2204.05909/) (2022)
- [Follow the STARs: Dynamic ω-Regular Shielding of Learned Policies](https://mlsanity.com/paper/2505.14689/) (2025)
- [Control Synthesis of Cyber-Physical Systems for Real-Time Specifications through Causation-Guided Reinforcement Learning](https://mlsanity.com/paper/2510.07715/) (2025)
- [Reward Machines for Signal Temporal Logic](https://mlsanity.com/paper/2608.13625/) (2026)

## Abstract (arXiv)

We propose a new specification language and control synthesis technique for single and multi-robot high-level tasks; these tasks include timing constraints and reaction to environmental events. Specifically, we define Event-based Signal Temporal Logic (STL) and use it to encode tasks that are reactive to uncontrolled environment events. Our control synthesis approach to Event-based STL tasks combines automata and control barrier functions to produce robot behaviors that satisfy the specification when possible. Our method automatically provides feedback to the user if an Event-based STL task can not be achieved. We demonstrate the effectiveness of the framework through simulations and physical demonstrations of multi-robot tasks.