Riemannian Geometry: Robot Motion Learning and Control

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Explores computational robot design, morphological intelligence, simulation, and innovative contact dynamics solutions.

Explores the basics of mobile robotics, emphasizing uncertainties and the Bayes filter algorithm.

Covers the structure of Riemannian manifolds, geodesic convexity, and the Riemannian distance function.

Covers Newton's method on Riemannian manifolds, focusing on second-order optimality conditions and quadratic convergence.

Explores the paradigm shift in robotic design towards highly predictable and programmable robots with augmented performance.

Covers the integration of visual perception and robotic actions in embodied AI.

Introduces Manopt, a toolbox for optimization on manifolds, focusing on solving optimization problems on smooth manifolds using the Matlab version.

Explores the optimization of paper planes and soft structures for thrust generation.

Explores Sensing in Human-Robot Interaction (HRI) and its applications, including human haptics, robot haptics, and modular artificial skin.

Explores scientific extrapolation in science fiction through intertextual narratives and semantic impertinence.

Explores feedback and adaptation in visual intelligence, enhancing machine performance in dynamic environments.

Covers optimization on manifolds, focusing on smooth manifolds and functions, and the process of gradient descent.

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

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