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Sequential Motion Primitives Recognition of Robotic Arm Task via Human Demonstration Using Hierarchical BiLSTM Classifier | IEEE Journals & Magazine | IEEE Xplore

Sequential Motion Primitives Recognition of Robotic Arm Task via Human Demonstration Using Hierarchical BiLSTM Classifier


Abstract:

Learning from demonstration (LfD) is an intuitive teaching technology without extensive programming for an operator. In recent LfD research, machine vision is usually use...Show More

Abstract:

Learning from demonstration (LfD) is an intuitive teaching technology without extensive programming for an operator. In recent LfD research, machine vision is usually used to capture the human-robot interaction. However, it's not reliable during the machining process. In this letter, a novel intuitive high-level kinesthetic teaching technology is proposed by reconstructing the motion information recorded from human-guided demonstrations. A hierarchical BiLSTM-based machine learning algorithm is proposed in this letter to recognize and segment motion primitives according to the therblig definition. A hybrid sensing interface is used to record and extract the motion features, consisting of the velocity profile, force/torque, and gripper information. The motion features are then used to classify into the target motion primitive by the proposed classifier. The experimental results and comparisons with the state-of-the-art algorithm show that the proposed method can correctly and efficiently synthesize the recorded motion features into a motion primitive sequence. Finally, the recognition results of real-world tasks show that the proposed algorithm can be used to reconstruct the human-guided task and further used to command a KUKA robot. The experimental results of the reconstructed trajectory show that a real-world task can represent and maintain the accuracy in 2.37 mm using the proposed algorithm.
Published in: IEEE Robotics and Automation Letters ( Volume: 6, Issue: 2, April 2021)
Page(s): 502 - 509
Date of Publication: 28 December 2020

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