Scaling Safe Multi-Agent Control for Signal Temporal Logic Specifications
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916559886221312 |
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| author | Eappen, Joe Xiong, Zikang Patel, Dipam Bera, Aniket Jagannathan, Suresh |
| author_facet | Eappen, Joe Xiong, Zikang Patel, Dipam Bera, Aniket Jagannathan, Suresh |
| contents | 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 . |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_05639 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scaling Safe Multi-Agent Control for Signal Temporal Logic Specifications Eappen, Joe Xiong, Zikang Patel, Dipam Bera, Aniket Jagannathan, Suresh Multiagent Systems Robotics I.2.9; I.2.11 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 . |
| title | Scaling Safe Multi-Agent Control for Signal Temporal Logic Specifications |
| topic | Multiagent Systems Robotics I.2.9; I.2.11 |
| url | https://arxiv.org/abs/2501.05639 |