The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
GelSight: High-Resolution Robot Tactile Sensors for Estimating Geometry and Force
Open accessAbstract
Tactile sensing is an important perception mode for robots, but the existing tactile technologies have multiple limitations. What kind of tactile information robots need, and how to use the information, remain open questions. We believe a soft sensor surface and high-resolution sensing of geometry should be important components of a competent tactile sensor. In this paper, we discuss the development of a vision-based optical tactile sensor, GelSight. Unlike the traditional tactile sensors which measure contact force, GelSight basically measures geometry, with very high spatial resolution. The sensor has a contact surface of soft elastomer, and it directly measures its deformation, both vertical and lateral, which corresponds to the exact object shape and the tension on the contact surface. The contact force, and slip can be inferred from the sensor’s deformation as well. Particularly, we focus on the hardware and software that support GelSight’s application on robot hands. This paper reviews the development of GelSight, with the emphasis in the sensing principle and sensor design. We introduce the design of the sensor’s optical system, the algorithm for shape, force and slip measurement, and the hardware designs and fabrication of different sensor versions. We also show the experimental evaluation on the GelSight’s performance on geometry and force measurement. With the high-resolution measurement of shape and contact force, the sensor has successfully assisted multiple robotic tasks, including material perception or recognition and in-hand localization for robot manipulation.
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Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Biomimetic tactile sensor array
Wettels, N., Santos, V. J., Johansson, R. S., & Loeb, G. E. (2008) · Advanced Robotics · doi:10.1163/156855308x314533 · Cited by 348
Cited by
More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch
Calandra, R., Owens, A., Jayaraman, D., Lin, J., Yuan, W., Malik, J., Adelson, E. H., & Levine, S. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2852779 · Cited by 359
Self-citationRecent Progress in Advanced Tactile Sensing Technologies for Soft Grippers
Qu, J., Mao, B., Li, Z., Xu, Y., Zhou, K., Cao, X., Fan, Q., Xu, M., Liang, B., Liu, H., et al. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202306249 · Cited by 259
Rearrangement: A Challenge for Embodied AI
Batra, D., Chang, A. X., Chernova, S., Davison, A. J., Deng, J., Koltun, V., Levine, S., Malik, J., Mordatch, I., Mottaghi, R., et al. (2020) · arXiv · doi:10.48550/arxiv.2011.01975 · Cited by 109
“A variety of sensor types are common: color vision cameras, depth sensors, contact/collision sensors, tactile sensors [50], LiDAR, microphones, and elastomeric sensors such as GelSight [38, 80] are prominent examples.”
BackgroundMultimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging
Erickson, Z., Xing, E., Srirangam, B., Chernova, S., & Kemp, C. C. (2020) · 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341165 · Cited by 50
VITO-Transformer: A Visual-Tactile Fusion Network for Object Recognition
Li, B., Bai, J., Qiu, S., Wang, H., & Guo, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3326241 · Cited by 39
Metadata
- Authors
- Yuan, W., Dong, S., & Adelson, E.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2017-11-29
- Volume
- 17
- Issue
- 12
- Pages
- 2762
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W2775635818
- Cited by
- 1,166
- References
- 2
- FWCI
- 21.52
- Type
- journal article
- Language
- en
- Open access
- gold
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