Robot manipulator control under the Active Inference framework

Abstract

The Active Inference framework is a neuroscience theory based on the Free Energy Principle by Karl Friston that has gained considerable prominence as a general theory to explain action and perception. Despite its promising future and use in different fields, its applicability for robot control has been barely investigated in the literature so far. This thesis aims at discerning if the current Active Inference implementation can be used for the control of robot manipulators and which are the requirements to do so. Firstly, an analysis of the current implementation of robot control under the Active Inference is made to understand its accomplishments and limitations. Secondly, both offline and online control schemes under the Active Inference framework are proposed. To continue with, a low-level controller is designed to be used in the Active Inference control scheme as a prior of the robot behaviour. The performance of the designed controller and the Active Inference scheme using this controller as a prior is compared to a benchmark controller. This comparison shows that the proposed implementation of the Active Inference scheme does not perform better than the current methods, but with further research on the how to implement this scheme using a real process it may become a valid alternative to these methods. Finally, recommendations on future work are given to bring this framework to online implementations and to verify the results obtained regarding the applicability of the Active Inference framework to robot manipulator control.