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arXiv · 1809.06268

Vision-based Teleoperation of Shadow Dexterous Hand using End-to-End Deep Neural Network

Shuang Li·Xiaojian Ma·Hongzhuo Liang·Michael Görner·Philipp Ruppel·Bing Fang·Fuchun Sun·Jianwei Zhang
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Abstract

In this paper, we present TeachNet, a novel neural network architecture for intuitive and markerless vision-based teleoperation of dexterous robotic hands. Robot joint angles are directly generated from depth images of the human hand that produce visually similar robot hand poses in an end-to-end fashion. The special structure of TeachNet, combined with a consistency loss function, handles the differences in appearance and anatomy between human and robotic hands. A synchronized human-robot training set is generated from an existing dataset of labeled depth images of the human hand and simulated depth images of a robotic hand. The final training set includes 400K pairwise depth images and joint angles of a Shadow C6 robotic hand. The network evaluation results verify the superiority of TeachNet, especially regarding the high-precision condition. Imitation experiments and grasp tasks teleoperated by novice users demonstrate that TeachNet is more reliable and faster than the state-of-the-art vision-based teleoperation method.

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Shuang Li, Xiaojian Ma, Hongzhuo Liang, Michael Görner, Philipp Ruppel, Bing Fang, Fuchun Sun, Jianwei Zhang. 2018-09-17. Vision-based Teleoperation of Shadow Dexterous Hand using End-to-End Deep Neural Network. https://arxiv.org/abs/1809.06268

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