PhD Researcher, University of Udine
Matteo Dunnhofer is a postdoctoral researcher at the Machine Learning and Perception Lab of the University of Udine, Italy. From the same institute, he received the BSc and MSc in Computer Science in 2016 and 2018, and the PhD in Industrial and Information Engineering in 2022. In 2018, he has been visiting student at the Australian Centre for Robotic Vision hosted at the Queensland University of Technology, Australia.
His research focuses on the use of deep learning to tackle computer vision problems, especially for visual object tracking. He is also interested in applying deep learning techniques in the context of medical image analysis and video-based sport analytics. On these topics, he published several papers that appeared in international journals and conferences, and he organized workshops and tutorials. In 2021, he has been awarded for winning the Visual Object Tracking VOT2021 Long-term Challenge.
Deep Reinforcement Learning for Robot Control
Reinforcement Learning (RL) is a promising paradigm to optimise machine learning models with limited supervision. In recent years, such a technique has experienced a successful
resurgence in the research community, primarily due to their demonstrated effectiveness in optimizing deep neural networks.
Among the many applications currently benefiting RL methods, there is robot control. The underlying idea is to model the controller of a robot using a neural network, establish a high-level
goal for the robot, and utilize RL to autonomously learn a control behavior that enables the robot to achieve the objective. This lab lecture will deal this problem. A neural network will be trained via state-of-the-art RL optimisation algorithms to provide the control parameters of a robotic arm, enabling it to pursue simple goals within a simulated environment. To
provide a comprehensive understanding of RL problems, the tutorial will begin by presenting the fundamental concepts. Subsequently, it will demonstrate the utilization of the most recent open-source software tools to solve the problem at hand.