Can LLMs speed up multi-robot behavior tree planning?
MRBTP: Efficient Multi-Robot Behavior Tree Planning and Collaboration
This paper introduces MRBTP, a new algorithm for coordinating multiple robots using Behavior Trees (BTs). It addresses challenges like coordinating diverse robot actions and avoiding redundant work. Crucially for LLM-based systems, MRBTP offers a plugin where an LLM can pre-plan "subtrees" of actions, effectively suggesting larger-scale strategies for each robot. This accelerates planning and improves teamwork by reducing the need for constant communication during execution. The LLM interacts through a structured JSON format, receiving feedback to refine its suggested subtrees. Experiments show improved efficiency and robustness, particularly when the LLM-generated subtrees and inter-robot communication are combined.