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.
|IOP Conference Series: Materials Science and Engineering
|Published - Mar 22 2017
|1st Annual International Conference on Information Technology and Digital Applications 2016, ICITDA 2016 - Yogyakarta, Indonesia
Duration: Nov 14 2016 → Nov 16 2016
ASJC Scopus subject areas
- General Materials Science
- General Engineering