2026-04-13T14:44:58Zhttps://recercat.cat/oai/requestoai:recercat.cat:2117/1183522025-07-22T18:03:49Zcom_2072_1033col_2072_452951
RECERCAT
author
Canal Anton, Oleguer
2018-05
In the context of robotic object manipulation, this work presents a simple regrasp policy based on the tactile feedback captured by the "fingers" of the robot gripper. To do so, there is a learning based function that assesses the quality of a grasp and another model based methodology that searches for better grasping points. These algorithms have been tested on a wide variety of unknown objects, obtaining a significant grasp success improvement.Outgoing
http://creativecommons.org/licenses/by-nc-sa/3.0/es/ Open Access
Àrees temàtiques de la UPC::Matemàtiques i estadística
Mathematics
Statistics
Robotic manipulation
Unknown object picking
Tactile feedback
Regrasp policy
Estadística
Matemàtica
Classificació AMS::00 General::00A General and miscellaneous specific topics
Learning based regrasp policy from tactile feedback
Bachelor thesis