Large Language Models (LLMs) are gaining popularity in the field of robotics.
However, LLM-based robots are limited to simple, repetitive motions due to the
poor integration between language models, robots, and the environment. This
paper proposes a novel approach to enhance the performance of LLM-based
autonomous manipulation through Human-Robot Collaboration (HRC). The approach
involves using a prompted GPT-4 language model to decompose high-level language
commands into sequences of motions that can be executed by the robot. The
system also employs a YOLO-based perception algorithm, providing visual cues to
the LLM, which aids in planning feasible motions within the specific
environment. Additionally, an HRC method is proposed by combining teleoperation
and Dynamic Movement Primitives (DMP), allowing the LLM-based robot to learn
from human guidance. Real-world experiments have been conducted using the
Toyota Human Support Robot for manipulation tasks. The outcomes indicate that
tasks requiring complex trajectory planning and reasoning over environments can
be efficiently accomplished through the incorporation of human demonstrations.

通过人机协作的方式，本文提出了一种增强基于大型语言模型的自主操作的方法，并应用于机器人的高级语言指令解析、运动规划和理解环境。在与人类的互动过程中，通过结合远程操作和动态运动原理实现机器人从人类引导中学习。通过实验表明，在复杂轨迹规划和环境推理方面，基于大型语言模型的机器人通过融入人类示范可以高效地完成任务。