Estimating object hardness with a GelSight touch sensor
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IROS16_1542_FI.pdf
Size
3.79 MB
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Adobe PDF
Checksum (MD5)
d02155530fca3d0fcf3d6436c2e5ec83
Author(s)
Yuan, Wenzhen
Srinivasan, Mandayam A
Adelson, Edward H
Date Issued
December 2016
Journal
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Yuan, Wenzhen et al. “Estimating Object Hardness with a GelSight Touch Sensor.” 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 9-14 2016, Daejeon, South Korea, Institute of Electrical and Electronics Engineers (IEEE), December 2016 © 2016 Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
Abstract
Hardness sensing is a valuable capability for a robot touch sensor. We describe a novel method of hardness sensing that does not require accurate control of contact conditions. A GelSight sensor is a tactile sensor that provides high resolution tactile images, which enables a robot to infer object properties such as geometry and fine texture, as well as contact force and slip conditions. The sensor is pressed on silicone samples by a human or a robot and we measure the sample hardness only with data from the sensor, without a separate force sensor and without precise knowledge of the contact trajectory. We describe the features that show object hardness. For hemispherical objects, we develop a model to measure the sample hardness, and the estimation error is about 4% in the range of 8 Shore 00 to 45 Shore A. With this technology, a robot is able to more easily infer the hardness of the touched objects, thereby improving its object recognition as well as manipulation strategy.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Laboratory for Human and Machine Haptics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/IROS.2016.7759057
