Self-Healing RPA Systems: Machine Learning Approaches

Abstract

Robotic process automation (RPA) is being adopted as the flagship technology for optimizing rule-based repetitive tasks in virtually all industries. On the other hand, conventional RPA may not be able to handle unforeseen errors due to changes in the software environment, data format, or user interface. A prospective solution to this challenge could be the integration of a self-healing feature powered by machine learning. Thus, focusing on ML-driven solutions for real-time failure detection and automated repair, this study undertakes a holis-tic review of current advancements toward self-healing RPA systems. Beyond semantic matching, predictive maintenance, and anomaly detection, this study also explores the avenues through which supervised, unsupervised, and reinforcement learning can bolster resilience in RPA. Significant recent advances toward satisfying dynamic execution conditions using natural language processing, deep learning, and explainable artificial intelligence are looked at with a view toward frameworks that leverage these techniques. Current constraints like data shortage, interpretability, and model drift are noted as the study outlines future directions for use and study to take this study towards guiding practitioners and researchers in developing robust, intelligent, and adaptive RPA systems that can autonomously manage operational disruptions.