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相关实验视频

Updated: Jul 15, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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OHO:一个多模式,多用途的数据集,用于人机对象的交换.

Benedict Stephan1, Mona Köhler1, Steffen Müller1

  • 1Neuroinformatics and Cognitive Robotics Lab, Technische Universität Ilmenau, 98693 Ilmenau, Germany.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
概括
此摘要是机器生成的。

创建对象交换 (OHO) 数据集使机器学习能够实现安全的人机器人协作. 这一数据集有助于区分手和物体,用于关键的机器人抓取任务.

关键词:
6D姿势估计估计自动标签的自动化标签数据集数据集数据集这是一个交付-交付.语义细分 语义细分 语义细分 语义细分热图片 热图片 热图片

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相关实验视频

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 安全的人机协作需要在物体交付期间将手与物体区分开来.
  • 目前的方法缺乏对现实世界交付场景的强有力的解决方案.

研究的目的:

  • 介绍对象交换 (OHO) 数据集,用于开发机器学习模型.
  • 在安全关键的机器人任务中实现强大的手和物体区分.

主要方法:

  • 收集了用颜色,深度和热成像来使用手持物体的数据集.
  • 开发了用于点云和图像数据的自动标签生成,例如细分.
  • 训练并评估了一个实例分段模型,用于每像素手对象区分.

主要成果:

  • 该OHO数据集支持实例细分,3D姿势估计和形状估计.
  • 自动化标签被证明适用于训练有效的实例细分模型.
  • 基线实验证明了成功的手对象区分.

结论:

  • OHO数据集是促进人机交互和安全研究的宝贵资源.
  • 开发的实例细分管道显示了对现实世界机器人应用的前景.