人与机器人的平面共操纵扩展的对象:数据驱动的模型和控制从人-人双
Erich Mielke1, Eric Townsend1, David Wingate1
1Robotics and Dynamics Laboratory, Brigham Young University, Mechanical Engineering, Provo, UT, United States.
Frontiers in neurorobotics
|February 27, 2024
概括
这项研究使用人与人的交互数据来预测人类在机器人辅助操纵中的意图. 这种方法可以帮助机器人理解所需的动作,使人机协作更加顺利.
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
- 控制理论 控制理论
背景情况:
- 人与机器人在操纵大型物体方面的合作是具有挑战性的,因为运动模两可.
- 现有的方法很难在物理共同操纵任务中准确地推断出人类的意图.
研究的目的:
- 为了利用人类-人类二元数据来预测人类-机器人共同操纵中的动作意图.
- 开发和评估用于机器人辅助操作任务的新型控制器,其动作目标含糊不清.
主要方法:
- 分析人与人对象操纵中的扭矩模式,以识别意图信号.
- 开发一个深度神经网络,使用人人运动数据来预测未来的物体轨迹.
- 整合力和运动数据,以实时控制人机器人在人机器人二合体.
- 评估控制器在三度自由度平面运动任务中的性能.
主要成果:
- 在人对人数据中发现了侧向运动的明显扭矩触发器,这表明了意图.
- 一个深度神经网络有效地根据人类对人类试验的过去数据预测了未来的物体运动.
- 开发的控制器在模糊的人机器人联合操纵场景中表现出有效的性能.
结论:
- 人与人交互数据为推断人机协作中的动作意图提供了宝贵的见解.
- 数据驱动的方法,包括深度学习,可以显著改善机器人控制的物理共操作.
- 提出的方法提高了人机物理交互的直观性和效率.
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