Jonathan Asensio

Universitat Politècnica de València, Universitat Politècnica de Catalunya

Papers

Total Citations

H-Index

About

Jonathan Asensio is a robotics researcher whose work centers on intelligent control systems, machine learning applications in robotics, and computer vision-assisted automation. His research spans over two decades, reflecting a sustained commitment to advancing how robotic systems learn and adapt to complex tasks. Asensio's most notable contributions lie in the development of neural network-based feedforward control strategies for multi-joint robotic systems. His work addresses a fundamental challenge in robot learning: while systems can effectively reduce tracking errors for previously learned trajectories, they struggle to generalize that knowledge to new paths. His research proposes innovative solutions that leverage neural network prediction to bridge this gap, enabling robots to apply prior learning data more broadly and efficiently — a meaningful step toward more adaptive and intelligent robotic control. His earlier work in real-time vision systems demonstrated a forward-thinking approach to integrating sensory feedback into robotic learning environments, establishing foundational ideas that informed his later neural network research. While Asensio's citation counts remain modest — with his top work accumulating four citations — his research represents careful, methodical contributions to a highly specialized field, offering valuable insights for roboticists and AI researchers working at the intersection of control theory and machine learning.