Marie Claire Capolei

TL;DR: This work embedded a cerebellum-based control system into a humanoid robot that becomes capable of handling dynamical external and internal complexity and built the canonical cerebellar microcircuit by combining machine learning and computational neuroscience techniques.

TL;DR: A novel methodology to artificially replicate these learning and adaptive principles into a robotic feedback controller that combines machine learning, artificial neural network, and computational neuroscience techniques to deal with all the nonlinearities and complexities that modern robotic systems could present.

TL;DR: A novel bio-inspired modular control architecture that merges a recurrent cerebellar-like loop for adaptive control and a Smith predictor controller is proposed to provide accurate anticipatory corrections to the generation of the motor commands in spite of sensory delays and to validate the robustness of the proposed control method to input and physical dynamic changes.

TL;DR: This research proposes a comparative study between two bio-inspired control architectures for quadruped legged robots where learning takes place either during the evolutionary search or only after that, and the performance of both systems has been analyzed by changing the robot dynamics and its interaction with the external environment.

TL;DR: Results show that the contribution provided by Cerebellar learning leads to an optimization of the performance with errors being reduced by 30% compared with the case where the cerebellar contribution is not applied.