Robot training using system identification

Gait Training using Pneumatically Actuated Robot System

Towards modelling complex robot training tasks through system identification

Previous research has shown that sensor-motor tasks in mobile robotics applications can be modelled automatically, using NARMAX system identification, where the sensory perception of the robot is mapped to the desired motor commands using nonlinear polynomial functions, resulting in a tight coupling between sensing and acting — the robot responds directly to the sensor stimuli without having in...

Robot control system using SMR signals detection

One of the important issues in designing a brain-computer interface system is to select the type of mental activity to be imagined. In some of these systems, mental activity varies with user intent and action that must be controlled by the brain-computer system, and in a number of other signals, the received signals contain the same activity-related mental activity that should be performed by t...

Model Identification and Analysis in Robot Training

Robot training is a fast and efficient method of obtaining robot control code. Many current machine learning paradigms used for this purpose, however, result in opaque models that are difficult, if not impossible to analyse, which is an impediment in safety-critical applications or application scenarios where humans and robots occupy the same workspace. In experiments with a Magellan Pro mobile...

Nonlinear System Identification using Evolutionary Computing based Training Schemes

The present work deals with application of recently developed evolutionary computing based training methods for non-linear system identification problem. Generally, most of the systems are nonlinear in nature. The conventionally used standard derivative based identification scheme does not work satisfactorily for nonlinear systems, which is due to premature settling of the model parameters. To ...