数据:WireFishing-M:使用触觉,视觉和自身感知传感器进行可变形电缆插入的多式数据集.
Tianyu Zhou1, Hengxu You1, Fang Xu1
1The Informatics, Cobots and Intelligent Construction (ICIC) Lab, University of Florida, 1949 Stadium Rd, Gainesville, FL 32611, USA.
Data in brief
|October 27, 2025
概括
本研究介绍了WireFishing-M,这是一个用于机器人操纵研究的多式联络数据集. 它具有使用触摸感应和多视图视频的电缆插入任务,用于开发先进的机器人控制.
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:
- 可变形物体操纵和触觉感应对于先进的机器人能力至关重要.
- 现有的数据集往往缺乏复杂的多式联络任务的多式联络数据.
研究的目的:
- 介绍WireFishing-M,这是一个用于机器人插入电线的新型多式联网数据集.
- 支持可变形物体操纵,触觉感知和机器人学习方面的研究.
- 为机器人领域的多式模式模型开发提供一个基准.
主要方法:
- 使用了一个带有Allegro手和DIGIT触觉传感器的Franka Emika Panda机器人.
- 捕获了同步的GelSight触摸图像,多视图RGB视频和终端效应器姿势.
- 包括七种电缆类型,具有不同的物理性质,适用于各种插入场景.
主要成果:
- 数据集包括全面的同步传感器数据,包括触觉,视觉和姿势信息.
- WireFishing-M促进了在机器人电缆插入过程中接触相互作用的研究.
- 数据被组织起来,以便在机器人学习和模拟框架中直接使用.
结论:
- WireFishing-M是推动机器人操纵和触觉感应研究的宝贵资源.
- 该数据集可以开发和对复杂的机器人任务进行多模式AI模型的基准测试.
- 公共可用性促进了更广泛的采用,并加速了现场的进展.
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