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