How to safely control thousands of robots in a cluttered environment?
Decentralized Safe and Scalable Multi-Agent Control under Limited Actuation
September 17, 2024
https://arxiv.org/pdf/2409.09573This paper proposes MA-ICBF, a novel framework for controlling multiple AI agents (e.g., robots) safely and efficiently in a shared space. It combines classical optimization techniques with the learning capabilities of neural networks.
Key features relevant to LLM-based multi-agent systems:
- Safety Guarantee: Uses Integral Control Barrier Functions (ICBFs) to mathematically guarantee collision avoidance, even with many agents.
- Limited Action Handling: Addresses real-world limitations where agents have restricted actions, crucial for physical robots or constrained LLMs.
- Deadlock Prevention: Analyzes and minimizes situations where agents get stuck due to conflicting goals, a common problem in multi-agent settings.
- Scalability: Employs a combination of learning and efficient optimization (log-sum-exp trick) to handle a large number of agents, up to 1024 in their tests.
- Generalization: Demonstrates effectiveness when trained on a smaller number of agents and then deployed in scenarios with many more.