Teaching Winter 2024

Students will present papers from a list of important recent works in machine learning compiled by the professor. Topics include deep and reinforcement learning, and transformer and GNN architectures. The focus will be in the use of these techniques in simple applications that are crisp and easy to understand. The list of papers will also include those underlying well-known learning systems like as Dalle-E, ChaptGPT, Alphazero, Gato, etc.

Task and Motion Planning (TAMP) deals with combining high-level decision making with low-level manipulation in robotics. In the lab course, we implement and evaluate multiple state-of-the-art TAMP approaches in a domestic service robot scenario. You will learn about key concepts of robotics such as task planning with PDDL, motion planning algorithms, as well as integrated task and motion planning (TAMP).