LLM-BRAIn：大規模言語モデルによるロボット行動ツリーの高速自動生成
LLM-BRAIn: AI-driven Fast Generation of Robot Behaviour Tree based on Large Language Model
Alpaca 7Bをファインチューニングし、オペレータのテキスト指示からロボットの行動ツリーをオンボードで生成する手法を提案。生成された行動ツリーは人間作成のものと区別が難しいほど自然で、未学習の指示にも対応できる。
著者: Artem Lykov, Dzmitry Tsetserukou
分類: cs.RO
原文アブストラクト
This paper presents a novel approach in autonomous robot control, named LLM-BRAIn, that makes possible robot behavior generation, based on operator's commands. LLM-BRAIn is a transformer-based Large Language Model (LLM) fine-tuned from Stanford Alpaca 7B model to generate robot behavior tree (BT) from the text description. We train the LLM-BRAIn on 8,5k instruction-following demonstrations, generated in the style of self-instruct using text-davinchi-003. The developed model accurately builds complex robot behavior while remaining small enough to be run on the robot's onboard microcomputer. The model gives structural and logical correct BTs and can successfully manage instructions that were not presented in training set. The experiment did not reveal any significant subjective differences between BTs generated by LLM-BRAIn and those created by humans (on average, participants were able to correctly distinguish between LLM-BRAIn generated BTs and human-created BTs in only 4.53 out of 10 cases, indicating that their performance was close to random chance). The proposed approach potentially can be applied to mobile robotics, drone operation, robot manipulator systems and Industry 4.0.
