ViTac: Feature Sharing Between Vision and Tactile Sensing for Cloth Texture Recognition
Name
1802.07490.pdf
Description
Submitted version
Size
2.73 MB
Format
Unknown
Checksum (MD5)
6dd0da88562b6ba87662861f45a3d896
Author(s)
Luo, Shan
Yuan, Wenzhen
Adelson, Edward H
Cohn, Anthony G.
Fuentes, Raul
Date Issued
May 2018
Publisher
IEEE
Citation
Luo, Shan, Yuan, Wenzhen, Adelson, Edward, Cohn, Anthony G. and Fuentes, Raul. 2018. "ViTac: Feature Sharing Between Vision and Tactile Sensing for Cloth Texture Recognition."
Version
Original manuscript
Abstract
© 2018 IEEE. Vision and touch are two of the important sensing modalities for humans and they offer complementary information for sensing the environment. Robots could also benefit from such multi-modal sensing ability. In this paper, addressing for the first time (to the best of our knowledge) texture recognition from tactile images and vision, we propose a new fusion method named Deep Maximum Covariance Analysis (DMCA) to learn a joint latent space for sharing features through vision and tactile sensing. The features of camera images and tactile data acquired from a GelSight sensor are learned by deep neural networks. But the learned features are of a high dimensionality and are redundant due to the differences between the two sensing modalities, which deteriorates the perception performance. To address this, the learned features are paired using maximum covariance analysis. Results of the algorithm on a newly collected dataset of paired visual and tactile data relating to cloth textures show that a good recognition performance of greater than 90% can be achieved by using the proposed DMCA framework. In addition, we find that the perception performance of either vision or tactile sensing can be improved by employing the shared representation space, compared to learning from unimodal data.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icra.2018.8460494
