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相关概念视频

Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...

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相关实验视频

Updated: May 8, 2026

Measurement of Spatial Stability in Precision Grip
09:36

Measurement of Spatial Stability in Precision Grip

Published on: June 4, 2020

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基于学习的对象的刚度和形状估计与多指手抓握的信心水平.

Kyo Kutsuzawa1, Minami Matsumoto1, Dai Owaki1

  • 1Neuro-Robotics Laboratory, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, Japan.

Frontiers in neurorobotics
|December 4, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的方法,用于多指手实时估计物体的刚性和形状. 该系统使用循环神经网络来量化估计信心,增强机器人操纵.

关键词:
深度学习是一种深度学习.抓住抓住抓住抓住抓住概率学推理推理的概率学推理.自己的感觉 (proprioception),就是感觉.机器人手是一个机器人手.形状估计,形状估计.硬度估计 硬度估计

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Last Updated: May 8, 2026

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 机器学习 机器学习
  • 人与计算机的交互

背景情况:

  • 人类通过触摸直观地估计物体的属性,如刚性和形状.
  • 评估对这些估计的信心对于灵巧的操纵至关重要.
  • 当前的机器人系统往往缺乏实时财产估计和信任评估的强有力的方法.

研究的目的:

  • 开发一种用于多指的机器人手来估计物体刚度和形状的方法.
  • 用自感信号来测量与这些估计相关的信心水平.
  • 实施一个实时的学习框架,不确定性意识的财产估计.

主要方法:

  • 开发了一个基于概率推理的学习框架.
  • 实施的循环神经网络 (RNN) 用于实时估计.
  • 利用自身感知信号 (关节角度,速度) 进行信心测量.

主要成果:

  • 训练有素的神经网络可以准确地估计物体的刚度和形状.
  • 该系统通过差异和量量化估计信心.
  • 展示了表达不确定性和任务难度的能力.

结论:

  • 拟议的方法可以对机器人手进行可靠的状态估计.
  • 这种方法可以与灵活的对象操纵策略集成.
  • 在机器人技术中促进基于概率推理的决策.