Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.
Covers learning and adaptive control for robots, focusing on real-time reactivity and path planning using dynamical systems.
Covers challenges and solutions for robots to work safely with humans, emphasizing adaptability and predictability.
Explores obstacle avoidance using Dynamical Systems for robots, focusing on modulation, stability guarantees, and contraction theory.
Explores two paradigms for robotics, components like motors, gears, sensors, and controllers, and design considerations for various types of robots.
Delves into the definition of robots, emphasizing their programmability and ability to perform complex actions.
Covers the programming steps to make a robot turn green and move forward when a button is pressed.
Explores the concept of robots, defined as mechatronic devices designed to automatically perform tasks imitating human actions.
Explores hierarchical control concepts in robotics, including switching, prioritizing, and information chains, to achieve complex robot behaviors.
Explores learning and adaptive control for robots, emphasizing modulation of dynamical systems to improve stability and enable reactive motion.
