Research of the Robot's Learning Effectiveness in the Changing Environment

TL;DR

This paper investigates adaptive algorithms that enable robotic systems to effectively learn and adapt to changing environments, focusing on goal alignment and target achievement in allocation problems.

Contribution

It explores the potential of target adaptation algorithms to improve robotic learning effectiveness in dynamic environments.

Findings

Adaptive algorithms enhance goal reflection in robotic actions.

Target adaptation improves success in allocation tasks.

The approach supports effective learning in changing environments.

Abstract

The object of the research is the adaptive algorithms that are used by the operator when educating the robotic systems. Operator, being the target-setting subject, is interested in the goal that robotic systems, being the conductor of his targets (criteria), would provide a maximum effectiveness of these targets' (criteria's) achievement. Thus, the adaptive algorithms provide the adequate reflection of the operator's goals, found in the robotic systems' actions. This work considers potential possibilities of such target adaption of the robotic systems used for the class of the allocation problems.