基金会模型有助于人类机器人协作组装.
Yuchen Ji1, Zequn Zhang2, Dunbing Tang1
1College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, People's Republic of China.
Scientific reports
|October 22, 2024
概括
这项研究引入了一个新的人机协作 (HRC) 系统,使用基础模型 (FMs) 来增强感知和推理. 这种方法可以实现灵活的,通用的任务完成,而无需对新对象或任务进行再培训.
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
- 制造业 制造技术 制造技术
背景情况:
- 现有的人机协作 (HRC) 系统在环境感知和任务推理方面扎,需要专门的模型和对新对象或任务的再培训.
- 目前的HRC方法仅限于预定义的任务,无法处理未定义的推理,阻碍了灵活性和概括性.
研究的目的:
- 提出一种新的HRC方法,利用基础模型 (FMs),特别是大型语言模型 (LLMs) 和视觉基础模型 (VFMs).
- 克服当前HRC系统在环境感知和任务推理方面的局限性,以提高灵活性和通用性.
主要方法:
- 在HRC中使用快速学习的LLM用于未定义任务推理.
- 集成了各种VFM用于场景语义感知,无需重新训练模型.
- 开发了一个全面的基于FM的HRC系统,包括感知,推理和执行模块.
主要成果:
- 通过广泛的实验,在感知和推理方面展示了FM的卓越性能.
- 在零件组装案例研究中验证了基于FM的HRC系统的可行性和有效性.
- 与现有方法相比,实现了更灵活和更普遍的HRC能力.
结论:
- 基础模型提供了一个强大的解决方案,以提高HRC系统的感知和推理能力.
- 拟议的基于FM的HRC系统为定制制制造任务提供了灵活和通用的方法.
- 这项工作为制造业中更具适应性和智能的人机协作铺平了道路.
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