This paper develops an enhanced teaching interface tested on both a Baxter robot and a KUKA iiwa robot. Movements are collected from a human demonstrator by using a Kinect v2 sensor, and then the data is sent to a remote PC for the teleoperation with Baxter. Meanwhile, data is saved locally for the playback process of the Baxter. The dynamic movement primitive (DMP) is used to model and generalize the movements. In order to learn from multiple demonstrations accurately, dynamic time warping (DTW), is used to pretreat the data recorded by the robot platform and Gaussian mixture model (GMM), aiming to generate multiple patterns after the teaching process, are employed for the calculation of the DMP. Then the Gaussian mixture regression (GMR) algorithm is applied to generate a synthesized trajectory with smaller position errors in 3D space. This proposed approach is tested by performing two tasks on a KUKA iiwa and a Baxter robot.

中文翻译：

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使用DMP和GMR的机器人的增强型教学界面。

本文开发了一种在Baxter机器人和KUKA iiwa机器人上均经过测试的增强型教学界面。使用Kinect v2传感器从人类演示者收集动作，然后将数据发送到远程PC，以与Baxter进行遥控操作。同时，数据被本地保存以用于百特的回放过程。动态运动原语（DMP）用于对运动进行建模和概括。为了准确地从多个演示中学习，使用动态时间规整（DTW）对机器人平台记录的数据进行预处理，并采用高斯混合模型（GMM）进行计算，目的是在教学过程后生成多个模式DMP。然后应用高斯混合回归（GMR）算法生成3D空间中位置误差较小的合成轨迹。