LLM-as-BT-Planner: Leveraging LLMs for Behavior Tree Generation in Robot Task Planning
September 16, 2024 Β· Declared Dead Β· π IEEE International Conference on Robotics and Automation
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Authors
Jicong Ao, Fan Wu, Yansong Wu, Abdalla Swikir, Sami Haddadin
arXiv ID
2409.10444
Category
cs.RO: Robotics
Citations
25
Venue
IEEE International Conference on Robotics and Automation
Last Checked
4 months ago
Abstract
Robotic assembly tasks remain an open challenge due to their long horizon nature and complex part relations. Behavior trees (BTs) are increasingly used in robot task planning for their modularity and flexibility, but creating them manually can be effort-intensive. Large language models (LLMs) have recently been applied to robotic task planning for generating action sequences, yet their ability to generate BTs has not been fully investigated. To this end, we propose LLM-as-BT-Planner, a novel framework that leverages LLMs for BT generation in robotic assembly task planning. Four in-context learning methods are introduced to utilize the natural language processing and inference capabilities of LLMs for producing task plans in BT format, reducing manual effort while ensuring robustness and comprehensibility. Additionally, we evaluate the performance of fine-tuned smaller LLMs on the same tasks. Experiments in both simulated and real-world settings demonstrate that our framework enhances LLMs' ability to generate BTs, improving success rate through in-context learning and supervised fine-tuning.
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