TY - JOUR
T1 - Development of a neuromorphic control system for a lightweight humanoid robot
AU - Folgheraiter, Michele
AU - Keldibek, Amina
AU - Aubakir, Bauyrzhan
AU - Salakchinov, Shyngys
AU - Gini, Giuseppina
AU - Franchi, Alessio Mauro
AU - Bana, Matteo
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2017/3/22
Y1 - 2017/3/22
N2 - 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 reproduce complex periodic trajectories. Each module 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 0.74.
AB - 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 reproduce complex periodic trajectories. Each module 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 0.74.
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U2 - 10.1088/1757-899X/185/1/012021
DO - 10.1088/1757-899X/185/1/012021
M3 - Conference article
AN - SCOPUS:85017475993
SN - 1757-8981
VL - 185
JO - IOP Conference Series: Materials Science and Engineering
JF - IOP Conference Series: Materials Science and Engineering
IS - 1
M1 - 012021
T2 - 1st Annual International Conference on Information Technology and Digital Applications 2016, ICITDA 2016
Y2 - 14 November 2016 through 16 November 2016
ER -