Scaling Safe Multi-Agent Control for Signal Temporal Logic Specifications

Joe Eappen, Zikang Xiong, Dipam Patel, Aniket Bera, Suresh Jagannathan

Keywords: Multi-Robot Systems, Path Planning for Multiple Mobile Robots or Agents, Collision Avoidance, Hybrid Logical/Dynamical Planning and Verification, Deep Learning Methods

TL;DR: Graphical Neural Networks (GNNs) can help scale Specification-driven control on Multi-agent systems beyond existing Mixed Integer Linear Programming (MILP)-based planners.

Abstract: Existing methods for safe multi-agent control using logic specifications like Signal Temporal Logic (STL) often face scalability issues. This is because they rely either on single-agent perspectives or on Mixed Integer Linear Programming (MILP)-based planners, which are complex to optimize. These methods have proven to be computationally expensive and inefficient when dealing with a large number of agents. To address these limitations, we present a new scalable approach to multi-agent control in this setting. Our method treats the relationships between agents using a graph structure rather than in terms of a single-agent perspective. Moreover, it combines a multi-agent collision avoidance controller with a Graph Neural Network (GNN) based planner, models the system in a decentralized fashion, and trains on STL-based objectives to generate safe and efficient plans for multiple agents, thereby optimizing the satisfaction of complex temporal specifications while also facilitating multi-agent collision avoidance. Our experiments show that our approach significantly outperforms existing methods that use a state-of-the-art MILP-based planner in terms of scalability and performance.

Video: https://youtu.be/MYnEQjd3fF8

Website: https://jeappen.github.io/mastl-gcbf-website/

Code: https://github.com/jeappen/mastl-gcbf

Student Paper: yes

Submission Number: 486