How to build robust controllers for LLM robot collectives?
Synthesising Robust Controllers for Robot Collectives with Recurrent Tasks: A Case Study
This paper explores automatically creating robust cleaning schedules for teams of robots in buildings like schools. It uses a simplified model of the environment and robot capabilities, focusing on navigation, battery life, and room contamination levels. A partially observable Markov decision process (POMDP) is used for strategy synthesis, allowing for uncertainty in contamination levels. This synthesized strategy is then used to generate a concrete cleaning schedule. Scalability is addressed by using partial observability and composing robot movements concurrently.
Key points for LLM-based multi-agent systems: The use of POMDPs for strategy synthesis offers a way to handle uncertainty in multi-agent environments, a common challenge with LLMs. The simplified model, focusing on key variables and abstracting away details, is relevant to managing the complexity of LLM-based multi-agent simulations. The focus on synthesizing a high-level strategy and then generating a concrete schedule could be applicable to coordinating actions in LLM-based multi-agent systems. Finally, the paper highlights challenges in scalability and parameter selection, which are directly relevant to working with complex LLM-based multi-agent applications.