Machine Learning Based Compensation

Machine learning (ML) is increasingly used to compensate for various system imperfections and uncertainties across diverse applications. Current research focuses on developing ML models, including neural networks and reinforcement learning algorithms, to address issues like robotic joint failures, sensor inconsistencies, and human biases in human-robot interaction. This work aims to improve system robustness, accuracy, and efficiency by learning adaptive control strategies and predictive models that mitigate errors and enhance performance in complex, dynamic environments. The resulting advancements have significant implications for robotics, human-computer interaction, and other fields requiring reliable and adaptable systems.