LLMをビヘイビアツリー生成器として活用するロボット組立タスクプランニング
LLM-as-BT-Planner: Leveraging LLMs for Behavior Tree Generation in Robot Task Planning
大規模言語モデルを用いてロボット組立タスクのビヘイビアツリーを自動生成する枠組みを提案し、4種のインコンテキスト学習とファインチューニングで成功率を向上させた。
著者: Jicong Ao, Fan Wu, Yansong Wu, Abdalla Swikir, Sami Haddadin
分類: cs.RO
原文アブストラクト
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.
