Geometric Framework

Geometric frameworks are increasingly used to analyze and improve machine learning models and robotic systems, offering a powerful alternative to purely algebraic approaches. Current research focuses on applying geometric concepts like manifolds, canal surfaces, and Voronoi diagrams to address challenges in areas such as adversarial attacks, ensemble learning, active learning, and shared autonomy in robotics. These frameworks provide valuable insights into the underlying structure of data and models, leading to improved algorithms and a deeper understanding of complex systems. The resulting advancements have implications for various fields, including image classification, anomaly detection, and human-robot interaction.