通过上下文人类运动预测和意图推断来增强机器人的协作任务
Javier Laplaza1, Francesc Moreno1, Alberto Sanfeliu1
1Institut de Robòtica i Informàtica Industrial de Barcelona, Universitat Politècnica de Catalunya, C/ Llorens i Artigas 4-6, 08024 Barcelona, Catalonia Spain.
International journal of social robotics
|October 31, 2025
概括
这项研究引入了一种深度学习模型,用于预测人机协作中的3D人类运动和意图. 该模型通过考虑人类在机器人附近的行为来增强机器人导航和任务成功.
科学领域:
- 机器人和计算机视觉 机器人和计算机视觉
- 人工智能的人工智能
- 人与机器人的交互
背景情况:
- 目前的3D人类运动预测模型使用的数据集并不反映人类在机器人附近的运动.
- 这种数据分布差距限制了机器人在现实世界协作任务中的有效性.
- 结合任务上下文和人类合作意愿,可以提高预测准确性和机器人导航.
研究的目的:
- 提出一种深度学习架构,用于预测3D人体运动和人类在协作任务中的意图.
- 提高机器人的导航能力,并在协作场景中取得成功.
- 开发一个灵活的模型,适应各种任务,具有不同的输入要求.
主要方法:
- 开发了一个使用多头注意力机制的深度学习架构.
- 该模型将人体运动和任务上下文作为输入,以预测人体运动并推断人类的意图.
- 该架构在协作对象交付和葡萄收获任务上得到了验证,具有特定任务的输入变化.
主要成果:
- 拟议的架构成功地预测了3D人类运动,并推断了人类对协作任务的意图.
- 用户对交付任务的研究表明,在使用预测时,人类对机器人的社交性,自然性,安全性和舒适性的感知得到了改善.
- 该模型在协作葡萄收获任务中表现出有希望的结果,展示了其在现实世界应用中的潜力.
结论:
- 开发的深度学习架构有效地整合了人类运动预测和意图推断,以加强人机协作.
- 该模型在处理各种输入方面的灵活性使其适应各种现实世界的协作任务.
- 这项研究有助于在共享环境中实现更直观,安全和高效的人机交互.
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