基于LLM的机器人系统用于现实世界的应用:对其代理性和伦理性的审查
Emmanuel K Raptis1,2, Athanasios Ch Kapoutsis1,2, Elias B Kosmatopoulos1,2
1Information Technologies Institute, The Centre for Research and Technology Hellas, Thessaloniki, Greece.
Frontiers in robotics and AI
|September 4, 2025
概括
这项调查探讨了大型语言模型 (LLM) 如何在现实世界机器人中实现代理人工智能. 它对系统进行了分类,并介绍了评估机器人的自主性,安全性和道德性的框架.
科学领域:
- 机器人和人工智能
- 人与机器人的互动
- 机器学习的伦理
背景情况:
- 代理人工智能系统可以自主运行,在最小的人力投入的情况下做出决策并采取行动.
- 大型语言模型 (LLM) 提供了先进的推理和知识能力,为机器人自主提供了新的可能性.
- 将LLM集成到机器人系统中是一个快速发展的领域,具有重要的现实应用.
研究的目的:
- 在现实应用中提供基于LLM的机器人系统的全面调查.
- 引入一种新的分类系统来评估LLM驱动机器人的"真实性".
- 为这些先进的机器人系统建立道德和安全评估框架.
主要方法:
- 基于LLM的机器人系统在关键应用领域 (导航,操纵,多代理,多任务) 的系统分类.
- 基于自主性,目标导向性,适应性和决策能力的新型代理性分类的开发.
- 建立伦理,安全和透明度原则 (偏见,公平,稳定性,安全,监督,可解释性,可审计性,合规性) 的评估框架.
主要成果:
- 识别和分类各种由LLM驱动的机器人系统,展示验证的代理行为.
- 提出了一个新的指标来量化LLM驱动机器人的代理性水平.
- 突出了有关能力和道德保障的当前方法中的关键差距,紧张局势和权衡.
结论:
- 通过LLM集成, 机器人代理能力正在显著提升.
- 一个强大的框架对于评估和确保这些系统的道德和安全部署至关重要.
- 为了使越来越有能力的LLM驱动机器人与社会规范和价值观保持一致,继续进行研究至关重要.
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