大型语言模型驱动的自然语言交互控制框架,用于单操作员双手电话操作
Haolin Fei1, Tao Xue2, Yiyang He1
1School of Engineering, Lancaster University, Lancaster, United Kingdom.
Frontiers in robotics and AI
|August 1, 2025
概括
一个新的AI助手,双手远程操作大语言模型助手 (BTLA),使一个操作员能够使用语音命令和远程操作来控制两个机器人手臂. BTLA显著提高了任务的成功率,并减少了操作员的工作量.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人与计算机的交互
- 人工智能的人工智能
背景情况:
- 双手远程操作给单个操作员带来了重大的认知和协调挑战.
- 将控制分配给多个运营商可能会导致决策模两可,并降低效率.
研究的目的:
- 为了引入一个新的框架,双手远程操作大语言模型助理 (BTLA),旨在增强单操作员对两个机器人手臂的控制.
- 通过整合人工智能来增强操作员的能力,以实现直观的控制和任务执行.
主要方法:
- 该BTLA框架使用GPT-3.5轮模型来解释用于控制二级机器人臂的语音命令.
- 操作人员保持主臂的直接远程操作控制,同时为辅助臂发出语音命令.
- 该系统自主地从六个预定义的操纵技能中进行选择,包括镜像,轨迹跟踪和对象抓取.
主要成果:
- 与单独远程操作相比,BTLA显示任务覆盖率增加了76.1%,成功率增加了240.8%.
- 与双向对照相比,BTLA在覆盖率上获得了19.4%的收益,成功率上获得了69.9%.
- 操作员的心理工作量减少了38-52% (NASA-TLX),其中85%的人认为语音交互是自然和有效的.
结论:
- 通过提高操作员效率和减少认知负载,BTLA框架为双手电话操作提供了显著的进步.
- 人工智能驱动的语音命令集成为复杂的机器人控制任务提供了自然和有效的方法.
- 在各种应用中,BTLA代表了一种有前途的方法来改善人机协作.
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