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Real-time gestural control of robot manipulator through Deep Learning human-pose inference

Abstract : With the raise of collaborative robots, human-robot interaction needs to be as natural as possible. In this work, we present a framework for real-time continuous motion control of a real collabora-tive robot (cobot) from gestures captured by an RGB camera. Through deep learning existing techniques, we obtain human skeletal pose information both in 2D and 3D. We use it to design a controller that makes the robot mirror in real-time the movements of a human arm or hand.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-02276236
Contributor : Fabien Moutarde <>
Submitted on : Monday, September 2, 2019 - 1:58:01 PM
Last modification on : Wednesday, October 14, 2020 - 3:52:34 AM
Long-term archiving on: : Thursday, January 9, 2020 - 9:13:30 AM

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  • HAL Id : hal-02276236, version 1

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Jesus Bujalance, Fabien Moutarde. Real-time gestural control of robot manipulator through Deep Learning human-pose inference. Int. Conf. on Computer Vision Systems, Sep 2019, Thessalonique, Greece. ⟨hal-02276236⟩

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