1. Scaling Safe Multi-Agent Control for Signal Temporal Logic Specifications
- Author
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Eappen, Joe, Xiong, Zikang, Patel, Dipam, Bera, Aniket, and Jagannathan, Suresh
- Subjects
Computer Science - Multiagent Systems ,Computer Science - Robotics ,I.2.9 ,I.2.11 - 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. The project website is https://jeappen.com/mastl-gcbf-website/ and the code is at https://github.com/jeappen/mastl-gcbf ., Comment: Accepted to CoRL 2024. arXiv admin note: text overlap with arXiv:2401.14554 by other authors
- Published
- 2025