The widespread application of learning-based methods in robotics has allowed significant simplifications to controller design and parameter adjustment. In this article, robot motion is controlled with learning-based methods. A control policy using a broad learning system (BLS) for robot point-reaching motion is developed. A sample application based on a magnetic small-scale robotic system is designed without detailed mathematical modeling of the dynamic systems. The parameter constraints of the nodes in the BLS-based controller are derived based on Lyapunov theory. The design and control training processes for a small-scale magnetic fish motion are presented. Finally, the effectiveness of the proposed method is demonstrated by convergence of the artificial magnetic fish motion to the targeted area with the BLS trajectory, successfully avoiding obstacles.

中文翻译：

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一种利用小型类鱼机器人验证的广泛学习系统的机器人运动学习方法

基于学习的方法在机器人技术中的广泛应用极大地简化了控制器设计和参数调整。在本文中，机器人运动是通过基于学习的方法进行控制的。开发了一种使用广泛学习系统（BLS）进行机器人点到达运动的控制策略。基于磁性小型机器人系统的示例应用程序的设计没有对动态系统进行详细的数学建模。基于Lyapunov理论推导了基于BLS的控制器中节点的参数约束。介绍了小型磁力鱼运动的设计和控制训练过程。最后，人工磁鱼运动与BLS轨迹收敛到目标区域，成功避开障碍物，证明了该方法的有效性。