Learning Hand Movement by Markerless Demonstration for Humanoid Robot Task
نویسندگان
چکیده
In this paper, we investigate the problem of generating trajectories from demonstration and instructing the robot to perform similar tasks under new situations. We present an approach that enables robots to extract movement trajectories from Kinect data capture, automatically segment movement into action units, learn the task-specific models and grasp target object under new situations. The model is build on top of a set of Dynamic Movement Primitives. We successfully test our models on Baxter Robot.
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