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
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.
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.
相关概念视频
02:16Stereotype Content Model
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...
01:28Automatic Processing and Automatic Social Behavior
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...