Deep Reinforcement Learning for 3D-based Object Grasping - Vermelho, Ricardo André Galhardas (2022)
Nowadays, collaborative robots based on Artificial Intelligence algorithms are very common to see in workstations and laboratories and they are expected to help their human colleagues in their everyday work. However, this type of robots can also assist in a domestic home, in tasks such as separate and organizing cutlery objects, but for that they need an algorithm to tell them which object to grasp and where to it. The main focus of this thesis is to create or improve an existing algorithm based on a Deep Reinforcement Learning for 3D-based Object Grasping, aiming to help collaborative robots on such tasks. Therefore, this work aims to present the state of the art and the study carried out, that enables the implementation of the proposed model that will help such robots to detect, grasp and separate each type of cutlery objects and consecutive experiments and results, as well as the retrospective of all the work done.