Explores Bayesian disturbance injection for robust imitation in robot learning, demonstrating its effectiveness in reducing error compounding and achieving high task achievement.
Covers corrected exercises from the 2020 exam in the field of robotics, including topics such as accuracy, speed, DC motors, optimal gear ratio, dynamics of robot arms, encoders, and kinematics.
Presents a novel architecture for robot learning of haptic interaction, achieving robust object class estimation and enhancing haptic interaction efficiency.
Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.
