Visual task identification and characterization using polynomial models

Visual task identification and characterization using polynomial models

Developing robust and reliable control code for autonomous mobile robots is difficult, because the interaction between a physical robot and the environment is highly complex, subject to noise and variation, and therefore partly unpredictable. This means that to date it is not possible to predict robot behaviour based on theoretical models. Instead, current methods to develop robot control code ...

Visual Task Identification Using Polynomial Models∗

Developing robust and reliable control code for autonomous mobile robots is difficult, because the interaction between a physical robot and the environment is highly complex, subject to noise and variation, and therefore partly unpredictable. This means that to date it is not possible to predict robot behaviour based on theoretical models. Instead, current methods to develop robot control code ...

Process Identification Using Polynomial Models

Abstract: This paper deals with the identification of linear systems using input-output response data. Specifically we focus on nonparametric (finite impulse or step response, FIR or FSR) models widely used in model predictive control. A polynomial model is proposed to reduce the number of parameters needed to represent the model. This leads to parsimonious, yet extremely robust models. The tim...

Visual Task Inference Using Hidden Markov Models

It has been known for a long time that visual task, such as reading, counting and searching, greatly influences eye movement patterns. Perhaps the best known demonstration of this is the celebrated study of Yarbus showing that different eye movement trajectories emerge depending on the visual task that the viewers are given. The objective of this paper is to develop an inverse Yarbus process wh...

Identification of nonlinear systems using Polynomial Nonlinear State Space models