Thesis of Amaury Depierre

Deep Learning For Adaptative Robotic Grasping


Industrial bin picking (extraction of one piece from a stack) is more and more used in many areas (automotive, food, waste treatment ...) and is a complex task to automate. A bin picking system should indeed be able to analyze the data of a visual sensor, to extract a potential grasp and then to transpose it into actions performed by the robot. The current approaches work but they still need a human parametrization for each processed object.

To deal with the increasing automation of these tasks, it is necessary to minimize human interventions when introducing new object in the system. Our main goal is to use deep learning techniques to allow the robot to use its previously acquired knowledge on new unseen objects. This will lead to more autonomous and adaptive robotic systems.

Advisor: Liming Chen
Coadvisor: Emmanuel Dellandréa

Defense date: friday, may 21, 2021

Mr Peters Jan PetersProfesseur(e)TU Darmstadt -FB-Informatik -FG-IAS -Hochschulstr. 10 -64289 DarmstadtRapporteur(e)
Mr Calinon SylvainDirecteur(trice) de rechercheIdiap Research Institute -Centre du Parc -Rue Marconi 19 -CH-1920 Martigny -SwitzerlandRapporteur(e)
Mme Perdereau VéroniqueProfesseur(e)ISIR -4 place Jussieu -CC 173 -75252 Paris cedex 05Président(e)
Mr Grard MatthieuDocteurSiléane -17 Rue Descartes -42000 Saint-ÉtienneExaminateur​(trice)
Mme Sciutti AlessandraChercheurIstituto Italiano di Tecnologia -Via EnricoMelen 83 -Building B -16152 Genova -ItalyExaminateur​(trice)
Mr Dellandréa EmmanuelMaître de conférenceECL LyonCo-directeur (trice)
Mr Chen LimingProfesseur(e)ECL LyonDirecteur(trice) de thèse