期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 4, 期 2, 页码 2101-2107出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2019.2899192
关键词
Force and tactile sensing; biomimetics; deep learning in robotics and automation
类别
资金
- Leverhulme Trust [RL-2016-39]
- NVIDIA Corporation
Deep learning has the potential to have same the impact on robot touch as it has had on robot vision. Optical tactile sensors act as a bridge between the subjects by allowing techniques from vision to be applied to touch. In this letter, we apply deep learning to an optical biomimetic tactile sensor, the TacTip, which images an array of papillae (pins) inside its sensing surface analogous to structures within human skin. Our main result is that the application of a deep convolutional neural network can give reliable edge perception, and, thus a robust policy for planning contact points to move around object contours. Robustness is demonstrated over several irregular and compliant objects with both tapping and continuous sliding, using a model trained only by tapping onto a disk. These results relied on using techniques to encourage generalization to tasks beyond which the model was trained. We expect this is a generic problem in practical applications of tactile sensing that deep learning will solve.
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