整合大型语言模型,以实现直观的机器人导航
Ziheng Xue1, Arturs Elksnis1, Ning Wang2
1Bristol Robotics Laboratory, University of Bristol, University of the West of England, Bristol, United Kingdom.
Frontiers in robotics and AI
|September 22, 2025
概括
本研究介绍了一种使用量化大型语言模型 (LLM) 的人工智能代理框架,以改进家庭辅助机器人. 该系统增强了自然语言的理解和在资源有限的环境中导航.
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
- 人与计算机的交互
背景情况:
- 家庭辅助机器人与自然语言交互,物体检测和在资源有限的环境中导航扎.
- 这些局限性阻碍了家庭机器人助理的实际部署和有效性.
研究的目的:
- 提出使用大型语言模型 (LLM) 的人工智能代理框架,以应对家庭辅助机器人的挑战.
- 在资源有限的设备上实现强大的自然语言交互和导航能力.
主要方法:
- 开发了一个人工智能代理框架,包括EnvNet,RoutPlanner和AIBrain.
- 利用量化大型语言模型 (LLM) 来降低计算要求.
- 整合组件用于自然语言理解和空间导航.
主要成果:
- 在家庭环境中表现出更好的自然语言理解.
- 为家庭辅助机器人实现了更高的导航精度.
- 展示了在资源有限的硬件上部署基于LLM的代理的可行性.
结论:
- 拟议的基于LLM的AI代理框架为增强家庭辅助机器人的能力提供了可行的解决方案.
- 量子化的LLM对于在资源有限的国内环境中实现高级AI功能是有效的.
- 这项研究为智能家居辅助机器人的实际部署提供了宝贵的基础.
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