机器人需要知道的关于操作的一切:创建可操作的知识图表来支持机器人准备饭菜
Michaela Kümpel1, Manuel Scheibl2, Jan-Philipp Töberg3
1Institute for Artificial Intelligence, Bremen University, Bremen, Germany.
Frontiers in robotics and AI
|November 14, 2025
概括
这项研究引入了可操作的知识图 (AKG),将自然语言的食谱转化为机器人操作,用于自主准备饭菜. 这使得机器人能够更独立地执行复杂的任务.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 机器人目前缺乏自主准备用自然语言指令自主准备食物的能力.
- 弥合人类可以理解的食谱和机器人执行之间的差距是一个重大挑战.
研究的目的:
- 开发一种新的方法,使机器人能够通过解释自然语言的食谱指令来自主准备食物.
- 使用可操作知识图 (AKG) 将配方指令映射到机器人操纵任务中.
主要方法:
- 利用可操作的知识图 (AKG) 将配方指令分类为六个核心机器人操纵任务 (Action Cores).
- 使用 Recipe1M+ 数据集来分析动作动词,并通过直接匹配和等号相似性将它们映射到动作核心.
- 使用大语言模型的神经符号方法来处理不匹配的动词并生成新的动作核心.
主要成果:
- 在将配方行动动词与定义的行动核心相匹配时,实现了76.5%的覆盖率.
- 证明了AKG在适应一般计划以满足特定机器人任务方面的多功能性.
- 通过实验性食准备场景成功验证了方法.
结论:
- 可操作的知识图为将食谱转化为机器人行动提供了一个多功能框架.
- 这种方法为适应性机器人系统奠定了基础,能够在最小的人类干预下完成复杂的任务.
- 神经符号方法提高了系统解释和执行更广泛的配方指令的能力.
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