Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.
Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.
Delves into using simulations for Human-Robot Interaction, learning from human expertise and preferences, user models, system models, simulation results, and assisting drone landings.
Covers the use of transformers in robotics, focusing on embodied perception and innovative applications in humanoid locomotion and reinforcement learning.
Covers rotation matrices, translations, and direct geometric modeling of serial robots, including the Denavit-Hartenberg parameters and the sequence of movements for a 6 DOF robot.
Covers the internship information session for the Microengineering and Robotics master program at EPFL, emphasizing the value of internships for students and companies.
Explores turning bumper cars into unbumping ones through collision avoidance algorithms and the challenges faced when implementing ellipsoid barrier functions.
