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Explores Bayesian disturbance injection for robust imitation in robot learning, demonstrating its effectiveness in reducing error compounding and achieving high task achievement.
Introduces a project-based course in communications and robotics, emphasizing practical projects and independent learning to prepare students for real-world challenges.
Delves into using simulations for Human-Robot Interaction, learning from human expertise and preferences, user models, system models, simulation results, and assisting drone landings.
Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.
Delves into human-robot interactions through interpersonal manual tasks, revealing insights on performance differences and adaptation in human behavior.
Explores advancements in robot learning for autonomy at scale, covering deep learning challenges, efficient architecture, benchmarking results, and societal implications.
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.
Explores safe learning in robotics, covering the state of the art, open challenges, and vision in the field, emphasizing the importance of interdisciplinary collaboration.
Investigates sensorimotor control in locomotion using robots and mathematical models, exploring bimodal locomotion, insect walking, and undulatory swimming.
