Author:
Guillaume Jouffroy
Affiliation:
Artificial Intelligence Laboratory, University Paris 8, France
Keyword(s):
Joint constraint method, oscillatory recurrent neural network, generalized teacher forcing, feedback, adaptive systems.
Related
Ontology
Subjects/Areas/Topics:
Cybernetics
;
Health Engineering and Technology Applications
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Neural Networks Based Control Systems
;
NeuroSensing and Diagnosis
;
Neurotechnology, Electronics and Informatics
;
Robotics and Automation
Abstract:
In the robotics field, a lot of attention is given to the complexity of the mechanics and particularly to the number of degrees of freedom. Also, the oscillatory recurrent neural network architecture is only considered as a black box, which prevents from carefully studying the interesting features of the network’s dynamics. In this paper we describe a generalized teacher forcing algorithm, and we build a default oscillatory recurrent neural network controller for a vehicle of one degree of freedom. We then build a feedback system as a constraint method for the joint. We show that with the default oscillatory controller the vehicle can however behave correctly, even in its transient time from standing to moving, and is robust to the oscillatory controller’s own transient period and its initial conditions. We finally discuss how the default oscillator can be modified, thus reducing the local feedback adaptation amplitude.