Neural network-based modeling of robot action effects in conceptual state space of real world

Abstract

This paper gives the concept of an autonomous robotic agent that is capable of showing both machine learning and reactive behavior. The first methodology is used to collect information about the environment and to plan robot actions based on this information while the robot is performing tasks. Processing and storing information obtained during several task executions is called lifelong learning. Reactive behavior, the second desirable feature of an autonomous robot, is needed to execute the actions in a dynamically changing environment. This paper presents the main components of the machine learning part of the autonomous robotic agent: the conceptual state generalization module that forms conceptual states as categories (clusters) of observation vectors obtained from the sensor system; and the robot behavior effects modeller that calculates the next conceptual state from the current state and an action obtained from the action planner.