Vision-based Teleoperation of Shadow Dexterous Hand using End-to-End Deep Neural Network
| Autor: | Li, Shuang, Ma, Xiaojian, Liang, Hongzhuo, Görner, Michael, Ruppel, Philipp, Fang, Bing, Sun, Fuchun, Zhang, Jianwei |
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| Rok vydání: | 2018 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Accepted to ICRA 2019. Shuang Li and Xiaojian Ma contributed equally to this work |
| Databáze: | arXiv |
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