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生物信号引导机器人通过人类远程操作演示来学习自适应性硬度.

Wei Xia1,2, Zhiwei Liao3, Zongxin Lu3

  • 1School of Mechanical Engineering, Shaanxi Polytechnic Institute, Xianyang 712000, China.

Biomimetics (Basel, Switzerland)
|June 25, 2025
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概括

这项研究引入了一种新的机器人学习框架,该框架使用人类肌肉信号 (sEMG) 来调整机器人的硬度在任务中. 这种生物信号引导的方法使机器人能够更直观地学习类似人类的操作能力.

关键词:
高斯混合模型 (GMM)高斯混合回归 (GMR)人类远程操作的演示.表面电肌图 (sEMG) 是指表面电肌图.变量的阻抗控制控制变量的阻抗控制.

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 人与机器人的交互
  • 机器学习 机器学习

背景情况:

  • 机器人从人类演示中学习,对于类似人类的能力至关重要.
  • 人类手臂肌肉激活与终点度相关,这是机器人任务的关键因素.

研究的目的:

  • 提出一个生物信号引导的机器人自适应性度学习框架.
  • 通过人类演示,使机器人卡特西安阻抗参数的直观规划成为可能.

主要方法:

  • 一个人类远程操作的实时硬度调节演示平台.
  • 一个双阶段的概率模型 (GMM,GMR) 时间运动和运动-sEMG相关性.
  • 在现实世界中进行实验,以验证框架的有效性.

主要成果:

  • 机器人成功地实现了卡特西安阻抗特征的在线适应.
  • 在接触丰富的任务中证明有效的技能转移.
  • 验证了肌肉激活和终点度之间的正相关性.

结论:

  • 拟议的框架提供了一个简单,直观的方法,用于机器人刚性学习.
  • 它绕过了复杂的示范前硬度识别或示范后补偿.
  • 使机器人能够更有效地获得类似人类的操作刚性.