2026-04-17T11:57:12Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/1183522025-07-22T18:03:49Zcom_2072_1033col_2072_452951

00925njm 22002777a 4500 dc Canal Anton, Oleguer author 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 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