FRANÇAIS
A showcase of ÉTS researchers’ publications and other contributions
SEARCH

Grasp stability assessment through unsupervised feature learning of tactile images

Downloads

Downloads per month over past year

Cockburn, Deen and Roberge, Jean-Philippe and Thuy-Hong-Loan, Le and Maslyczyk, Alexis and Duchaine, Vincent. 2017. « Grasp stability assessment through unsupervised feature learning of tactile images ». In 2017 IEEE International Conference on Robotics and Automation (ICRA) (Singapore, Singapore, May 29- June 03, 2017), pp. 2238-2244. Piscataway, NJ, USA : IEEE.
Compte des citations dans Scopus : 6.

[img]
Preview
PDF
Grasp-Stability-Assessment-through-Unsupervised-Feature-Learning-of-Tactile-Images.pdf

Download (1MB) | Preview

Abstract

Grasping tasks have always been challenging for robots, despite recent innovations in vision-based algorithms and object-specific training. If robots are to match human abilities and learn to pick up never-before-seen objects, they must combine vision with tactile sensing. This paper present a novel way to improve robotic grasping: by using tactile sensors and an unsupervised feature-learning approach, a robot can find the common denominators behind successful and failed grasps, and use this knowledge to predict whether a grasp attempt will succeed or fail. This method is promising as it uses only high-level features from two tactile sensors to evaluate grasp quality, and works for the training set as well as for new objects. In total, using a total of 54 different objects, our system recognized grasp failure 83.70% of time.

Item Type: Conference proceeding
Professor:
Professor
Duchaine, Vincent
Affiliation: Génie de la production automatisée
Date Deposited: 24 Mar 2017 16:39
Last Modified: 30 Aug 2017 20:43
URI: http://espace2.etsmtl.ca/id/eprint/14834

Actions (login required)

View Item View Item