Doctoral thesis defense in Computer Science by Ms. Hiba SEKKAT

Euromed University of Fes (UEMF) is pleased to inform the public of the

doctoral thesis defense in ”Computer Science”

The thesis defense will take place on Saturday October 26, 2024 at 10:00 AM at UEMF 

Location: the Main Hall of the Incubator (LOC001994) 

The thesis will be presented by Ms. Hiba SEKKAT

Under the title:

“Integrated Approaches in Robotic Systems: Advancing Grasping with Deep Reinforcement Learning and Digital Twin Simulations for Domestic and Humanoid Robots”

Abstract 

In the fast-evolving field of robotics, grasping capabilities of domestic and humanoid robots are crucial for integration into daily life; this has been especially highlighted during the COVID-19 pandemic which underscored the need for robots to assist with everyday tasks, when human contact was reduced, particularly for vulnerable populations such as the elderly and individuals with disabilities. Among the most important tasks to be performed by a robot are grasping tasks, enabling interaction with objects, and thus, the ability to pick up items, arrange places, and help with everyday chores. However, current robotic systems often find it hard to be adaptive and efficient when operating in natural environments, especially home-like environments, that are inherently dynamic and cluttered. Dealing with and overcoming these challenges would hence require innovation that enables dealing with the complexity and variability of realworld settings. This thesis thus takes up the critical challenges of grasping in robotic manipulation through the development of a high-fidelity digital twin simulation environment designed to facilitate the future integration of Deep Reinforcement Learning algorithms. Meanwhile, the development and testing of such algorithms using digital twin simulations would provide a highly accurate controlled environment, making sure the application on a real robot is well-prepared. Therein, the initial step involved a detailed review of state-of-the-art Reinforcement Learning algorithms to identify the most suitable one for robotic grasping tasks across various settings. From this review, we selected the Deep Deterministic Policy Gradient algorithm that is particularly well-suited for our work environment, leading to creating a novel grasping system with high accuracy compared to the benchmark methods. Building on these promising results, we developed an efficient simulation system—a high-fidelity digital twin of the Pepper robot created using the Robot Operating System 2 framework—that supports continuous enhancement and helps progress the field. With most movements having a mean absolute error within a range of 0.01–0.02 radians, this digital twin—validated by comparing joint angles in both simulation and real-world data—lends a robust simulation platform for the eventual integration of intricate machine-learning algorithms. These results show how Deep Reinforcement Learning and digital twin technologies can be combined to overcome current limitations and open the door for more capable and independent home robots. This work is thus one of the competitive methodological steps toward better robotic grasping and sets the stage for extensions into autonomous robotics. All of these findings have important implications for developing capacities in socially interacting robots, notably the Pepper robot, in improving overall quality of life and offering substantial benefits for vulnerable populations.

Abstract 

In the rapidly evolving field of robotics, the grasping capabilities of domestic and humanoid robots are crucial for integration into daily life; this was particularly highlighted during the COVID-19 pandemic, which emphasized the need for robots to help with everyday tasks when human contact was reduced, especially for vulnerable populations such as the elderly and people with reduced mobility. Among the most important tasks to be carried out by a robot are grasping tasks, which allow interaction with objects, thus picking them up, arranging spaces and assisting with daily chores. However, current robotic systems often struggle to adapt and be efficient when operating in natural environments, especially home environments, which are inherently dynamic and cluttered. To meet and overcome these challenges therefore requires innovations that address the complexity and variability of real-world environments. This thesis therefore addresses the critical challenges of grasping in robotic manipulation through the development of a high-fidelity digital twin simulation environment designed to facilitate the future integration of deep reinforcement learning algorithms. At the same time, the development and testing of such algorithms using digital twin simulations would provide a highly accurate controlled environment, ensuring that deployment on a real robot is well prepared. The initial step consisted of a detailed review of the state of the art of reinforcement learning algorithms to identify the one best suited to robotic grasping tasks in different contexts. From this study, we selected the Deep Deterministic Policy Gradient algorithm which is particularly well suited to our working environment, enabling us to create a new grasping system of high accuracy compared to benchmark methods. Building on these promising results, we developed an efficient simulation system — a high-fidelity digital twin of the Pepper robot created using the "Robot Operating System 2" framework — which enables continuous improvement and contributes to advancing the field. With most movements having a mean absolute error between 0.01 and 0.02 radians, this digital twin — validated by comparing joint angles in simulation and real data — provides a robust simulation platform for the eventual integration of complex machine learning algorithms. These results show how deep reinforcement learning and digital twin technologies can be combined to overcome current limitations and pave the way for more capable and independent home robots. This work therefore constitutes one of the competitive methodological steps toward better robotic grasping and opens the way to extensions in autonomous robotics. All these findings have important implications for the development of the capabilities of socially interactive robots, notably the Pepper robot, by improving overall quality of life and offering substantial benefits to vulnerable populations.

This thesis will be presented before the jury members: