通过LLM和KG提高服务机器人的意图预测和解释性
Jincao Zhou1, Xuezhong Su2, Weiping Fu2,3
1School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an, China. jzhou324@xaut.edu.cn.
Scientific reports
|November 6, 2024
概括
这项研究引入了服务机器人的新框架,以了解复杂环境中的人类意图. 大型语言模型和基于知识图的意图识别框架 (LKIRF) 提高了预测准确性和推理透明度.
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
- 人与机器人的交互
背景情况:
- 服务机器人越来越多地融入日常生活,需要精确的人类意图识别.
- 当前的意图识别方法在复杂,非结构化的环境中面临挑战.
- 有效的人类意图预测对于及时和适当的机器人服务提供至关重要.
研究的目的:
- 开发一个先进的框架,以提高服务机器人的意图识别能力.
- 在复杂和非结构化的环境中解决现有方法的局限性.
- 为了提高服务机器人中人类意图推理的准确性,透明度和可解释性.
主要方法:
- 提出了大型语言模型和基于知识图的意图识别框架 (LKIRF).
- 使用人类运动和环境数据构建了一个离线知识图 (KG).
- 通过实时交互开发了一个在线推理图,利用大型语言模型 (LLM) 进行解释.
主要成果:
- 与传统方法相比,LKIRF在各种场景中显示出更好的预测准确性.
- 该框架提高了意图推理过程的透明度.
- 拟议的方法显著提高了意图推理的解释性.
结论:
- 在复杂的环境中,LKIRF有效地提高了服务机器人的意图识别.
- 结合LLM和KG,为人机交互提供了一个强大的方法.
- 该框架为服务机器人提供了更准确,透明和可解释的意图预测.
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