Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.
Explores obstacle avoidance using Dynamical Systems for robots, focusing on modulation, stability guarantees, and contraction theory.
Covers learning and adaptive control for robots, focusing on real-time reactivity and path planning using dynamical systems.
Covers the programming of a robot to detect obstacles and change color.
Delves into the definition of robots, emphasizing their programmability and ability to perform complex actions.
Explores the concept of robots, defined as mechatronic devices designed to automatically perform tasks imitating human actions.
Covers challenges and solutions for robots to work safely with humans, emphasizing adaptability and predictability.
Explores learning and adaptive control for robots, emphasizing modulation of dynamical systems to improve stability and enable reactive motion.
Explores two paradigms for robotics, components like motors, gears, sensors, and controllers, and design considerations for various types of robots.
