# Multi-agent Motion Planning from Signal Temporal Logic Specifications

arXiv: https://arxiv.org/abs/2201.05247 · HTML: https://mlsanity.com/paper/2201.05247/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2022.

## Built on by (most cited first, 21 of 21)

- [AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers](https://mlsanity.com/paper/2306.06531/) (2023)
- [A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches](https://mlsanity.com/paper/2404.02817/) (2024)
- [Signal Temporal Logic Neural Predictive Control](https://mlsanity.com/paper/2309.05131/) (2023)
- [Exploring Embodied Multimodal Large Models: Development, Datasets, and Future Directions](https://mlsanity.com/paper/2502.15336/) (2025)
- [Code-as-Symbolic-Planner: Foundation Model-Based Robot Planning via Symbolic Code Generation](https://mlsanity.com/paper/2503.01700/) (2025)
- [TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching](https://mlsanity.com/paper/2505.00562/) (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)
- [Collision- and Reachability-Aware Multi-Robot Control with Grounded LLM Planners](https://mlsanity.com/paper/2505.20573/) (2025)
- [Pretrained Embeddings as a Behavior Specification Mechanism](https://mlsanity.com/paper/2503.02012/) (2025)
- [TACOS: Task Agnostic COordinator of a multi-drone System](https://mlsanity.com/paper/2510.01869/) (2025)
- [Learning to Estimate System Specifications in Linear Temporal Logic using Transformers and Mamba](https://mlsanity.com/paper/2405.20917/) (2024)
- [JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 Minutes](https://mlsanity.com/paper/2505.06771/) (2025)
- [GenAI-based Multi-Agent Reinforcement Learning towards Distributed Agent Intelligence: A Generative-RL Agent Perspective](https://mlsanity.com/paper/2507.09495/) (2025)
- [Runtime Monitoring of Perception-Based Autonomous Systems via Embedding Temporal Logic](https://mlsanity.com/paper/2605.12651/) (2026)
- [CatlNet: Learning Communication and Coordination Policies from CaTL+ Specifications](https://mlsanity.com/paper/2212.11792/) (2022)
- [Reinforcement learning with timed constraints for robotics motion planning](https://mlsanity.com/paper/2601.00087/) (2025)
- [GraSP-STL: A Graph-Based Framework for Zero-Shot Signal Temporal Logic Planning via Offline Goal-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2603.29533/) (2026)
- [Multi-Agent Planning with Spatio-Temporal and Topological Constraints using STL-GO](https://mlsanity.com/paper/2607.28679/) (2026)
- [Generalizable Multi-Agent Planning from Signal Temporal Logic Specifications via Diffusion](https://mlsanity.com/paper/2608.29490/) (2026)
- [Bayesian Active Learning for Intent Disambiguation in Interactive Robot Planning](https://mlsanity.com/paper/2609.34270/) (2026)

## Abstract (arXiv)

We tackle the challenging problem of multi-agent cooperative motion planning for complex tasks described using signal temporal logic (STL), where robots can have nonlinear and nonholonomic dynamics. Existing methods in multi-agent motion planning, especially those based on discrete abstractions and model predictive control (MPC), suffer from limited scalability with respect to the complexity of the task, the size of the workspace, and the planning horizon. We present a method based on em timed waypoints/ to address this issue. We show that timed waypoints can help abstract nonlinear behaviors of the system as safety envelopes around the reference path defined by those waypoints. Then the search for waypoints satisfying the STL specifications can be inductively encoded as a mixed-integer linear program. The agents following the synthesized timed waypoints have their tasks automatically allocated, and are guaranteed to satisfy the STL specifications while avoiding collisions. We evaluate the algorithm on a wide variety of benchmarks. Results show that it supports multi-agent planning from complex specification over long planning horizons, and significantly outperforms state-of-the-art abstraction-based and MPC-based motion planning methods. The implementation is available at https://github.com/sundw2014/STLPlanning.