基于大语言模型的抛光机器人声控运动再现

Yukiyoshi Tanaka, S. Katsura
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引用次数: 1

摘要

近年来,工业领域的专业技术人才短缺已成为一个重大的社会问题。为了解决这一问题,将技能转移给机器人一直备受关注。然而,他们并不熟悉机器人的控制,也很难通过数字命令或程序源代码来教授机器人的技能。为了使人机交互更加人性化,人们进行了大量的研究。在以往的研究中,机器人的任务过程是预先定义的,在任务执行过程中不会发生变化。我们利用运动复制系统和GPT-3(大型语言模型之一)开发了一个机器人系统。该系统不仅可以复制运动,还可以在执行过程中使用自然语言命令修改保存的运动。我们通过将该系统应用于抛光机器人来评估该系统,并确认在输入命令后使用的工件表面会发生变化。
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A Voice-Controlled Motion Reproduction Using Large Language Models for Polishing Robots
In recent years, the shortage of professionally skilled people in industrial fields has been a major social problem. To solve this problem, the transfer of skills to robots has been attracting much attention. However, they are not familiar with robot control, and hard to teach robots their skills by numerical commands or program source code. For more user-friendly human-robot interaction, a lot of studies have been conducted. In previous researches, robot task processes are pre-defined and not changed in task execution. We developed a robot system using the motion-copying system and GPT-3, one of the Large Language Models. This system can not only copy the motion but also modify saved motion in execution by using natural language commands. We evaluated the proposed system by applying it to polishing robots and confirmed that the surface of used workpieces was changed following to input commands.
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