Slip Detection with Combined Tactile and Visual Information
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1802.10153.pdf
Description
Submitted version
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1.27 MB
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Unknown
Checksum (MD5)
b3aa8d78f2f0c534676f801f2c56d3e8
Author(s)
Li, Jianhua
Dong, Siyuan
Adelson, Edward H
Date Issued
May 2018
Publisher
IEEE
Citation
Li, Jianhua, Dong, Siyuan and Adelson, Edward. 2018. "Slip Detection with Combined Tactile and Visual Information."
Version
Original manuscript
Abstract
© 2018 IEEE. Slip detection plays a vital role in robotic manipulation and it has long been a challenging problem in the robotic community. In this paper, we propose a new method based on deep neural network (DNN) to detect slip. The training data is acquired by a GelSight tactile sensor and a camera mounted on a gripper when we use a robot arm to grasp and lift 94 daily objects with different grasping forces and grasping positions. The DNN is trained to classify whether a slip occurred or not. To evaluate the performance of the DNN, we test 10 unseen objects in 152 grasps. A detection accuracy as high as 88.03 % is achieved. It is anticipated that the accuracy can be further improved with a larger dataset. This method is beneficial for robots to make stable grasps, which can be widely applied to automatic force control, grasping strategy selection and fine manipulation.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/icra.2018.8460495
