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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.
Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.
Explores robotic body enhancement with extra limbs and the challenges of neuroengineering in developing robotic extra fingers for restoration and augmentation.
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 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.
Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.
Explores the stability of Ordinary Differential Equations, focusing on solution dependence, critical data, linearization, and control of nonlinear systems.
Delves into the theory of material activation, proposing a unified mathematical framework to model how multiple stimuli can produce changes at the macroscopic level.
