Xia Wei, Liao Zhiwei, Lu Zongxin, Yao Ligang
School of Mechanical Engineering, Shaanxi Polytechnic Institute, Xianyang 712000, China.
Engineering Research Center of Composite Movable Robot, Universities of Shaanxi Province, Xianyang 712000, China.
Biomimetics (Basel). 2025 Jun 13;10(6):399. doi: 10.3390/biomimetics10060399.
Robot learning from human demonstration pioneers an effective mapping paradigm for endowing robots with human-like operational capabilities. This paper proposes a bio-signal-guided robot adaptive stiffness learning framework grounded in the conclusion that muscle activation of the human arm is positively correlated with the endpoint stiffness. First, we propose a human-teleoperated demonstration platform enabling real-time modulation of robot end-effector stiffness by human tutors during operational tasks. Second, we develop a dual-stage probabilistic modeling architecture employing the Gaussian mixture model and Gaussian mixture regression to model the temporal-motion correlation and the motion-sEMG relationship, successively. Third, a real-world experiment was conducted to validate the effectiveness of the proposed skill transfer framework, demonstrating that the robot achieves online adaptation of Cartesian impedance characteristics in contact-rich tasks. This paper provides a simple and intuitive way to plan the Cartesian impedance parameters, transcending the classical method that requires complex human arm endpoint stiffness identification before human demonstration or compensation for the difference in human-robot operational effects after human demonstration.
从人类示范中学习的机器人开创了一种有效的映射范式，用于赋予机器人类似人类的操作能力。本文基于人类手臂肌肉激活与端点刚度呈正相关这一结论，提出了一种生物信号引导的机器人自适应刚度学习框架。首先，我们提出了一个人机遥操作示范平台，使人类指导者能够在操作任务期间实时调制机器人末端执行器的刚度。其次，我们开发了一种双阶段概率建模架构，依次使用高斯混合模型和高斯混合回归来对时间-运动相关性和运动-表面肌电图关系进行建模。第三，进行了一项实际实验，以验证所提出的技能转移框架的有效性，证明机器人在接触丰富的任务中实现了笛卡尔阻抗特性的在线自适应。本文提供了一种简单直观的方法来规划笛卡尔阻抗参数，超越了传统方法，传统方法需要在人类示范之前进行复杂的人类手臂端点刚度识别，或者在人类示范之后补偿人机操作效果的差异。
