Cockburn, Deen, Roberge, Jean-Philippe, Thuy-Hong-Loan, Le, Maslyczyk, Alexis et 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 : 36.
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Résumé
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.
| Type de document: | Compte rendu de conférence |
|---|---|
| Professeur: | Professeur Roberge, Jean-Philippe Duchaine, Vincent |
| Affiliation: | Autres, Génie de la production automatisée |
| Date de dépôt: | 24 mars 2017 16:39 |
| Dernière modification: | 17 avr. 2020 16:05 |
| URI: | https://espace2.etsmtl.ca/id/eprint/14834 |
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