FAGANELLO, M. Nathan (2021) Robotic Grasping: Improving data quality for Deep Learning of robust grasp poses PRE - Research Project, ENSTA.



The grasping of objects by autonomous robots is still an important issue in robotics. Many methods have been tried, but today, learning, as in all fields, is the most popular and generates the most research. In order to train all these learning algorithms, it is necessary to have datasets with several thousand grasps, presenting diversity and labelled in a way consistent with the given problem. In the context of this study, the objective is to create a dataset allowing to train robust grasp prediction algorithms. To do this, I worked on the random creation of scenes including randomly placed objects as well as on the creation of random or non-random grasp positions. I also set up metrics for the analysis of grasps in order to measure their quality according to the robustness criterion. Finally, I propose a tool for setting up and simulating scenes and object grasp as well as grasp analysis tools. Unfortunately, this last point is not perfectly developed and still requires research so that it is perfectly operational.

Item Type:Thesis (PRE - Research Project)
Subjects:Information and Communication Sciences and Technologies
Mathematics and Applications
ID Code:8508
Deposited By:nathan Faganello
Deposited On:16 mars 2022 16:32
Dernière modification:16 mars 2022 16:32

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