Adaptive Learning in UiPath: Enhancing RPA for Continuous Improvement and Scalability

Abstract

This paper aims to extend the use of adaptive learning in UiPath to improve the usability of process automation (RPA). Substantial engineering RPA systems designed for law-based preparations have problems with turnover and flexibility. Introducing the possibility of operating with users’ feedback, UiPath bots can become ML-enabled systems and develop into more intelligent systems capable of learning on their own. The study explores the technical approaches and looks into how to use the UiPath AI Center to redeploy the retrained models and adopt adaptive strategies such as reinforcement learning. The elements of data input, feedback, and the cycle of recalibration of the model are defined in explicit detail. This paper also presents an example of use case testing with the quantitative indicators of accuracy and increase in performance speed. Adaptive learning in RPA is discussed in this paper as a shift toward further minimization of manual intervention and improving the robustness of the process. Such adjustments will cover adaptive training algorithm enhancement, data quality, and scalability issues.