TY - GEN
T1 - Estimating object hardness with a GelSight touch sensor
AU - Yuan, Wenzhen
AU - Srinivasan, Mandayam A.
AU - Adelson, Edward H.
N1 - The work is supported by a grant from Shell to EHA and ERC-2009-AdG 247041 to MAS. The authors would like to thank Erik Hemberg, Shaiyan Keshvari, Xinchen Ni, Andrea Censi, Alyanna Villapandos, Siyuan Dong for their suggestions and help in experiments.
PY - 2016/11/28
Y1 - 2016/11/28
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85006489008&partnerID=8YFLogxK
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U2 - 10.1109/IROS.2016.7759057
DO - 10.1109/IROS.2016.7759057
M3 - Conference contribution
AN - SCOPUS:85006489008
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 208
EP - 215
BT - IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016
Y2 - 9 October 2016 through 14 October 2016
ER -