The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
GelSlim: A High-Resolution, Compact, Robust, and Calibrated Tactile-sensing Finger
Open accessAbstract
This work describes the development of a high-resolution tactile-sensing finger for robot grasping. This finger, inspired by previous GelSight sensing techniques (Johnson and Adelson 2009), features an integration that is slimmer, more robust, and with more homogeneous output than previous vision-based tactile sensors. To achieve a compact integration, we redesign the optical path from illumination source to camera by combining light guides and an arrangement of mirror reflections. We parameterize the optical path with geometric design variables and describe the tradeoffs between the finger thickness, camera depth of field, and size of the tactile sensing area. The sensor sustains the wear from continuous use - and abuse - in grasping tasks by combining tougher materials for the compliant gel, a textured fabric skin, a structurally rigid body, and a calibration process that maintains homogeneous illumination and contrast of the tactile images during use. Finally, we evaluate the sensor's durability along four metrics that track the signal quality during more than 3000 grasping experiments.
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Citations
Cites
Analysis and Observations From the First Amazon Picking Challenge
Correll, N., Bekris, K. E., Berenson, D., Brock, O., Causo, A., Hauser, K., Okada, K., Rodriguez, A., Romano, J. M., & Wurman, P. R. (2018) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2016.2600527 · Cited by 425 · cited 1×
“In cluttered environments like a pantry or a warehouse storage cell, as in the Amazon Robotics Challenge [7], a robot faces the challenge of singulating target objects from a tightly-packed collection of items.”
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Cited by
Lambeta, M., Chou, P.-W., Tian, S., Yang, B., Maloon, B., Most, V. R., Stroud, D., Santos, R., Byagowi, A., Kammerer, G., et al. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2977257 · Cited by 553
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
Sparsh: Self-supervised touch representations for vision-based tactile sensing
Higuera, C., Sharma, A., Bodduluri, C. K., Fan, T., Lancaster, P., Kalakrishnan, M., Kaess, M., Boots, B., Lambeta, M., Wu, T., et al. (2024) · arXiv · doi:10.48550/arxiv.2410.24090 · Cited by 4
Metadata
- Authors
- Donlon, E., Dong, S., Liu, M., Li, J., Adelson, E., & Rodriguez, A.
- Venue
- 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publisher
- IEEE
- Published
- 2018-10
- Pages
- 1927-1934
- OpenAlex
- W2793598667
- Cited by
- 332
- References
- 1
- FWCI
- 34.84
- Type
- conference paper
- Language
- en
- Open access
- green
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- Donlon, E., Dong, S., Liu, M., Li, J., Adelson, E., & Rodriguez, A. (2018). GelSlim: A High-Resolution, Compact, Robust, and Calibrated Tactile-sensing Finger. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.1109/iros.2018.8593661
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