一个人类意图和运动预测框架用于以人为中心的数字双胞胎中的应用
Usman Asad1, Azfar Khalid2, Waqas Akbar Lughmani3
1Department of Mechanical Engineering, Capital University of Science and Technology, Islamabad 45750, Pakistan.
Biomimetics (Basel, Switzerland)
|October 28, 2025
概括
预测人类在制造业中的意图是人类与机器人的合作的关键. 使用滚动上下文窗口的新框架提高了预测准确性和效率,优于现有方法.
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:
- 在制造业中,人机协作需要理解人类的意图,以便无地执行任务.
- 当前的基础模型与复杂的任务预测和上下文意识的运动生成作斗争.
研究的目的:
- 提出一个模块化框架来结构任务知识和工程上下文丰富的提示.
- 引导视觉语言模型 (VLMs) 在半结构化制造环境中理解和预测人类意图.
主要方法:
- 开发了一个模块化框架,用于任务知识结构和上下文丰富的提示工程.
- 实施了使用历史和预测状态的滚动上下文窗口策略.
- 在三个不同复杂度的用例中评估了性能,包括自我中心的视频和历史数据.
主要成果:
- 确定了预测准确度和延迟之间的关键权衡.
- 滚动上下文窗策略平衡了性能和效率,优于单图像输入和上下文学习.
- 对复杂任务而言,以自我为中心的视频观看提高了10.7%的性能.
- 历史姿势,凝视数据和上下文示例增强了短期运动预测的准确性.
结论:
- 拟议的框架有效地提高了VLMs在制造业中预测人类意图的能力.
- 滚动上下文窗策略为平衡准确性和效率提供了一个实际的解决方案.
- 结合各种数据源,如以自我为中心的视频和历史传感器数据,对于先进的人机协作至关重要.
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