A Neuromorphic Motion Controller for a Biped Robot

Here we propose a neuromorphic control system for a medium size humanoid robot under development in the Robotics and Mechatronics Department at Nazarbayev University and in cooperation with Politecnico di Milano

A Neuromorphic Motion Controller for a Biped Robot

Here we propose a neuromorphic control system for a medium size humanoid robot under development in the Robotics and Mechatronics Department at Nazarbayev University and in cooperation with Politecnico di Milano

Development of a Neuromorphic Control System for a Biped Robot

Humanoid robots are developed around the world with the purpose to assist humans in their domestic and public activities and operate in unstructured and hazardous environments. To accomplish this effectively, intelligent humanoids should be autonomous, to accomplish high-level human tasks without help, and adaptable, to be able to react to dynamic changes and external disturbances in operating environments. The primary objective of this thesis is to investigate how biologically plausible methods such as reservoir computing and rewardmodulated learning can be used for generating robust sensory-motor outputs and achieving adaptability of the biped system. Recurrent neural networks architecture is studied on two robot systems: first is Asimo humanoid and second is biped developed at Nazarbayev University

Development of a Neuromorphic Control System for a Biped Robot

Humanoid robots are developed around the world with the purpose to assist humans in their domestic and public activities and operate in unstructured and hazardous environments. To accomplish this effectively, intelligent humanoids should be autonomous, to accomplish high-level human tasks without help, and adaptable, to be able to react to dynamic changes and external disturbances in operating environments. The primary objective of this thesis is to investigate how biologically plausible methods such as reservoir computing and rewardmodulated learning can be used for generating robust sensory-motor outputs and achieving adaptability of the biped system. Recurrent neural networks architecture is studied on two robot systems: first is Asimo humanoid and second is biped developed at Nazarbayev University

Development of a neuromorphic control system for a lightweight humanoid robot

A neuromorphic control system for a lightweight middle size humanoid biped robot built using 3D printing techniques is proposed. The control architecture consists of different modules capable to learn and autonomously reproduced complex periodic trajectories. Each modul is represented by a chaotic Recurrent Neural Network (RNN) with a core of dynamic neurons randomly and sparsely connected with fixed synapses . A set of read-out units with adaptable synapses realize a linear combination of the neurons output in order to reproduce the target signals. Different experiments were conducted to find out the optimal initialization for the RNN`s parameters. From simulation results, using normalized signals obtained from the robot model, it was proven that all the instances of the control module can learn and reproduce the target trajectories with an average RMS error of 1.63 and variance 0.7

Learning and executing rhythmic movements through chaotic neural networks: A new method for walking humanoid robots

We propose Chaotic Neural Networks (CNN) as an alternative to other models of the Central Pattern Generation (CPG) circuits, which have been developed in the last years for robotic applications. We develop a new Matlab implementation of CNN and study their computational and functional performances. We show our results on walking humanoid robots, both in simulation and on real robots. We discuss our porting of the CNN to the on-board controller of the robot, where we verify the temporal and spatial performance. In a final comparison against CPG the CNN appear as a promising method to improve the adaptability of the robot to dynamic situations