Design of a Central Pattern Generator Using Reservoir Computing for Learning Human Motion

To generate coordinated periodic movements, robot locomotion demands mechanisms which are able to learn and produce stable rhythmic motion in a controllable way. Because systems based on biological central pattern generators (CPGs) can cope with these demands, these kind of systems are gaining in su...

Full description

Saved in:
Bibliographic Details
Main Authors: Wyffels, F., Schrauwen, B.
Format: Conference Proceeding
Language:English
Subjects:
central pattern generators
Centralized control
Control systems
Humanoid robots
Humans
locomotion
Neurons
Recurrent neural networks
reservoir computing
Reservoirs
Robot kinematics
Robot sensing systems
Signal generators
Citations: Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To generate coordinated periodic movements, robot locomotion demands mechanisms which are able to learn and produce stable rhythmic motion in a controllable way. Because systems based on biological central pattern generators (CPGs) can cope with these demands, these kind of systems are gaining in success. In this work we introduce a novel methodology that uses the dynamics of a randomly connected recurrent neural network for the design of CPGs. When a randomly connected recurrent neural network is excited with one or more useful signals, an output can be trained by learning an instantaneous linear mapping of the neuron states. This technique is known as reservoir computing (RC). We will show that RC has the necessary capabilities to be fruitful in designing a CPG that is able to learn human motion which is applicable for imitation learning in humanoid robots.
DOI:10.1109/AT-EQUAL.2009.32