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.

Information

title:
Estimating object hardness with a gelsight touch sensor
author:
Wenzhen Yuan,
Mandayam A Srinivasan,
Edward H Adelson
citation:
Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on. IEEE, 2016.
shortcite:
IROS 2016
year:
2016
created:
2016-01-01
keyword:
adelson,
wenzhen
www:
http://persci.mit.edu/pub_abstracts/estimating-object-hardness-with-gelsight-sensor.html
pdf:
http://people.csail.mit.edu/yuan_wz/GelSight1/IROS16_1542_FI.pdf
pageid:
estimating-object-hardness-with-gelsight-sensor
type:
publication
 
pub_abstracts/estimating-object-hardness-with-gelsight-sensor.html.txt · Last modified: 2017/09/15 11:33 by elmer