Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

RGCNN-nnUNet: Recurrent group equivariant nnU-Net for robust brain tissue segmentation on stroke NCCT.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same author

Role of cell density and proximity in electroporation for tissue ablation.

Communications physics·2026
Same author

Large Language Models Evaluation of Medical Licensing Examination Using GPT-4.0, ERNIE Bot 4.0, and GPT-4o.

Bioengineering (Basel, Switzerland)·2026
Same author

Nanosensors as diagnostic tools: emerging concepts, opportunities, and design barriers.

Analytical methods : advancing methods and applications·2026
Same author

A Narrative Review of the Roles of Nursing in Addressing Sexual Dysfunction in Oncology Patients.

Current oncology (Toronto, Ont.)·2025
Same author

Micro-spring force sensors using conductive photosensitive resin fabricated via two-photon polymerization.

Microsystems & nanoengineering·2025

相关实验视频

Updated: Jul 2, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.6K

在使用机器学习对智能假肢手的自动对象抓取上.

Jethro Odeyemi1, Akinola Ogbeyemi1, Kelvin Wong1

  • 1Advanced Engineering Design Laboratory, Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada.

Bioengineering (Basel, Switzerland)
|February 23, 2024
PubMed
概括

本研究介绍了一种使用计算机视觉和机器学习进行假肢抓取的自动化方法. 软演员-关键 (SAC) 算法在假肢手握任务中取得了99%的成功,提高了自主性.

科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能
  • 生物医学工程 生物医学工程

背景情况:

  • 当前的假肢技术在自主抓取和用户体验方面面临挑战.
  • 在电子假肢中,精细运动控制通常需要广泛的用户培训,这限制了可用性.
  • 改善假肢自主性对于增强功能和接受度至关重要.

研究的目的:

  • 提出一种自动化方法来控制假肢的抓取.
  • 利用计算机视觉和机器学习来提高假肢的自主性.
  • 评估不同强化学习算法在自动抓取任务中的性能.

主要方法:

  • 利用基于计算机视觉的技术和机器学习算法.
  • 采用了三个强化学习算法:软行为者-批判性 (SAC),深度Q网络 (DQN) 和近接政策优化 (PPO).
  • 训练有素的代理人使用这些算法进行自动抓取任务.

主要成果:

  • 软演员-批评 (SAC) 算法在20万个时间步骤内表现出最高的99%成功率.
  • 发现对象的物理特征会影响代理人学习最佳抓取策略的能力.
  • 在成功实现自主抓取方面,SAC的表现优于DQN和PPO.
关键词:
计算机视觉 计算机视觉电动肌谱学 电动肌谱学手的手势手势手势机器学习是机器学习.假肢的使用方法

更多相关视频

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace
09:11

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

Published on: August 8, 2019

5.7K
A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

9.5K

相关实验视频

Last Updated: Jul 2, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.6K
Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace
09:11

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

Published on: August 8, 2019

5.7K
A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

9.5K

结论:

  • 软演员-批判 (SAC) 算法显示了开发智能假肢手的巨大潜力.
  • 这种自动化方法可以带来具有自动物体抓取能力的假肢手.
  • 这些发现表明了走向更直观和自主假肢控制的道路.