How can robot swarms learn better via communication?
Signaling and Social Learning in Swarms of Robots
This paper explores how robots in a swarm can learn and work together effectively, especially when they have to figure things out on the fly in a changing environment. It focuses on how communication helps them coordinate and adapt.
For LLM-based multi-agent systems, the key takeaway is that effective communication strategies are crucial for decentralized learning and execution. The paper proposes classifying communication methods based on information selection (how much information is shared) and physical abstraction (how abstractly the information is represented), ranging from simple bio-inspired signals to complex language-based communication using LLMs. It emphasizes the challenges of aligning individual robot goals with overall swarm performance, particularly when communication strategies also evolve. The use of LLMs offers promising avenues for more sophisticated communication, enabling more robust and adaptive swarm behavior, but presents challenges like grounding language in the real world, handling biases, and managing computational constraints.