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.

翻译：论文摘要：本文提出一种名为LLM-BRAIn的自主机器人控制新方法，该方法能够基于操作员指令实现机器人行为生成。LLM-BRAIn是基于Transformer架构的大语言模型，通过微调Stanford Alpaca 7B模型实现从文本描述生成机器人行为树。我们使用text-davinci-003以自指令风格生成的8,500条指令遵循型演示数据对LLM-BRAIn进行训练。该模型在生成复杂机器人行为的同时保持紧凑性，可在机器人板载微型计算机上运行。模型能够输出结构逻辑正确的行为树，并能成功处理训练集中未出现的指令。实验未发现LLM-BRAIn生成的行为树与人工构建行为树之间存在显著主观差异（平均而言，参与者仅在10次测试中正确区分LLM-BRAIn生成与人工构建行为树的次数为4.53次，表明其区分能力接近随机水平）。该方法可应用于移动机器人、无人机操作、机器人操纵系统及工业4.0等领域。