人机交互使用检索增强生成和微调与变压器神经网络在工业5.0的微调
1Department of Industrial Engineering, School of Engineering, Damghan University, Damghan, Iran. h.fazl@du.ac.ir.
Scientific reports
|August 10, 2025
概括
这项研究介绍了一种新的人工智能框架,使用回收增强生成 (RAG) 和变压器神经网络来增强机器人决策. 这种方法提高了在制造环境中人机协作中的灵活性和性能.
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
- 制造系统制造系统的制造
背景情况:
- 现代制造业依赖于人工智能 (AI) 来实现自动化和人机交互 (HRI).
- 传统的机器人系统在复杂的团队工作条件下往往缺乏灵活性和动态响应能力.
研究的目的:
- 提出一个新的框架,将检索增强生成 (RAG) 与微调的变压器神经网络集成在一起,用于先进的机器人决策.
- 为了提高机器人的灵活性,实时响应能力,以及在制造业中的人机器人协作.
主要方法:
- 开发一个模型,将RAG用于知识获取和Transformers用于优化.
- 实施基于遗憾的学习,使机器人能够从过去的错误中学习并改进未来的决策.
- 通过数字案例研究验证,将拟议系统与传统机器人系统进行比较.
主要成果:
- 拟议的RAG和基于变压器的框架证明了机器人决策和灵活性的提高.
- 基于遗憾的学习有助于机器人系统的可预测性能改进.
- 该系统有效地解决了可扩展性,微调性,多模式学习和人工智能驱动机器人的伦理考虑方面的挑战.
结论:
- 开发的框架为制造业的人机交互提供了一种动态和智能化的方法.
- 这项研究为工业5.0,智能制造和协作机器人技术的进步铺平了道路.
- 该研究为基于人工智能的人机器人制造系统提供了明确的实施策略.
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