How can LLMs coordinate robot navigation in tight spaces?
Opinion-Driven Decision-Making for Multi-Robot Navigation through Narrow Corridors
This paper tackles the problem of coordinating multiple robots navigating a narrow corridor without direct communication. It proposes a system where robots form "opinions" about the best passage order by observing each other's movements and predicting their intentions. These opinions are dynamically updated using a Nonlinear Opinion Dynamics (NOD) model, helping robots reach a consensus on the traversal order and avoid deadlocks.
For LLM-based multi-agent systems, this research is relevant because it demonstrates how agents can achieve coordinated behavior without explicit communication, relying instead on observed actions and inferred intentions. This is analogous to how LLMs in a multi-agent setting can potentially interpret each other’s generated text as actions and infer intent, enabling coordination through emergent behavior. The game reduction technique introduced, where agents only consider a subset of other agents for decision-making, is also valuable for LLM-based systems to improve scalability by limiting the computational complexity of interactions in a multi-agent environment. Finally, the use of strategies, opinions, and bias within the NOD model is highly pertinent to LLM-based agents, which can be similarly influenced by pre-defined strategies, dynamically adjust opinions based on interactions, and be subject to biases in their decision-making process.