Layered control: Motor control in vertebrates to robots

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Explores data-driven approaches to improve robot design, focusing on compliance, soft materials, and complex interactions.

Explores Physically-Consistent Gaussian Mixture Models for robot control and trajectory learning.

Covers the key elements of robotics, including perception, action, and interaction.

Explores two paradigms for robotics, components like motors, gears, sensors, and controllers, and design considerations for various types of robots.

Explores hierarchical control concepts in robotics, including switching, prioritizing, and information chains, to achieve complex robot behaviors.

Covers compliant and force control for robots using Dynamical Systems.

Explores embodied behavioral control in animals and robots through group presentations and hands-on exercises.

Covers learning and adaptive control for robots, focusing on real-time reactivity and path planning using dynamical systems.

Explores autonomy, sensor classification, and reactive architectures in mobile robotics using the perception-to-action loop.

Explores autonomy in mobile robotics, high-fidelity simulation, control architectures, and real-time emulation.