This research introduces LLM-as-BT-Planner, a framework that leverages large language models to automatically generate behavior trees for robotic assembly tasks, reducing manual programming effort.

• Transforms complex robotic assembly challenges into structured behavior trees (BTs)
• Enables robots to handle long-horizon tasks with intricate part relationships
• Provides modularity and flexibility in robot task planning
• Demonstrates how LLMs can generate sophisticated planning structures beyond simple action sequences

Why it matters: This approach significantly reduces the engineering effort required to program robots for manufacturing tasks, potentially accelerating automation in factories while making robots more adaptable to new assembly challenges.

LLM-as-BT-Planner: Leveraging LLMs for Behavior Tree Generation in Robot Task Planning