Estimating object hardness with a GelSight touch sensor
Wenzhen Yuan, Mandayam A. Srinivasan, Edward H. Adelson
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.
BibTeX
@inproceedings{iros2016_estimatingobject,
title = {Estimating object hardness with a GelSight touch sensor},
author = {Wenzhen Yuan and Mandayam A. Srinivasan and Edward H. Adelson},
booktitle = {IROS 2016},
year = {2016}
}