使用可扩展的触摸手套学习人类掌握的特征
Subramanian Sundaram1,2,3,4, Petr Kellnhofer5,6, Yunzhu Li5,6
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. subras@csail.mit.edu.
Nature
|May 31, 2019
概括
研究人员开发了一种低成本的触觉手套, 这种触觉数据有助于识别物体,估计重量,并了解人类的掌握机制,
科学领域:
- 机器人技术
- 感官神经科学
- 机器学习
背景情况:
- 机器人很难模仿人类的灵巧掌握和推断物体属性,
- 目前的机器人捕捉策略严重依赖于计算机视觉, 缺乏相当的触觉传感平台和大规模数据集.
- 由于无法记录和分析触觉信号, 无法理解触觉信息在人类掌握力学中的作用.
研究的目的:
- 开发一个可扩展的,低成本的触摸感应系统,用于在对象操作时捕获触摸信息.
- 创建一个大规模的触觉数据集来分析物体识别,重量估计和掌握模式.
- 研究触觉特征如何影响先进假肢,机器人抓手和人机交互的设计.
主要方法:
- 一个可扩展的触摸手套配备了548个传感器 (压电膜和导电线程).
- 一个大规模的触觉数据集,包括135,000,记录了人类与26个不同物体的互动.
- 使用深度卷积神经网络分析记录的触觉数据以进行对象识别和属性估计.
主要成果:
- 触摸手套成功地实现了手的统一感应, 在对象交互时捕获详细的触摸信息.
- 这种系统能够识别单个物体,估计其重量,并揭示与抓取相关的触觉模式.
- 对数据集的分析突出了不同手部区域在对象操纵过程中的关键对应.
结论:
- 在手上均分布的触觉传感器可以有效地识别物体,估计重量和捕捉握力.
- 开发的低成本触觉手套和数据集为研究人类和机器人的触觉信息提供了基础.
- 获得的洞察力可以显著推进更灵巧的假肢,机器人工具和直观的人机界面的开发.
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