In the process of Human-robot skill transfer, we require the robot to reproduce the trajectory of teacher and expect that the robot can generalize the learned trajectory. For the trajectory after generalization, we expect that the robot arm can accurately track. However, because the model of the robot can not be accurately obtained, some researchers have proposed using a neural network to approximate the unknown term. The parameters of the traditional RBF neural network are usually selected through the empirical and trial-and-error method, which maybe biased and inefficient. In addition, due to the end-effector of the mechanical arm trajectory will be constantly changing according to the needs of the task, when the neural network of compact set cannot contain the whole input vector, the neural network cannot achieve the ideal approximation effect. In this paper, the broad neural network is used to approximate the unknown terms of the robot. This method can reuse the motion controller that has been learned and complete other motions in the robot operating space without relearning its weight parameters. In this paper, the effectiveness of the proposed method is proved by the ultrasound scanning task.

在人类机器人技能转移的过程中，我们要求机器人重现教师的轨迹，并期望机器人能够概括所学的轨迹。对于泛化后的轨迹，我们希望机器人手臂可以准确地跟踪。但是，由于无法准确获得机器人的模型，因此一些研究人员提出使用神经网络来近似未知项。传统的RBF神经网络的参数通常是通过经验法和反复试验法来选择的，这些方法可能有偏差且效率低下。此外，由于机械臂轨迹的末端执行器会根据任务的需要不断变化，因此当紧凑集的神经网络不能包含整个输入矢量时，该神经网络就无法达到理想的逼近效果。在本文中，广泛的神经网络用于近似机器人的未知项。该方法可以重复使用已学习的运动控制器，并在不重新获得其重量参数的情况下完成机器人操作空间中的其他运动。本文通过超声扫描任务证明了该方法的有效性。