Miguel Molina-Moreno
Papers
1
Total Citations
4
H-Index
1
About
Miguel Molina-Moreno is a researcher at the forefront of assistive robotics and human-robot interaction, with a focus on decoding human intention through gaze and action analysis. His work centers on developing interpretable machine learning models that bridge the gap between human cognitive processes and robotic assistance. His most notable contribution, "Asymmetric Multi-Task Learning for Interpretable Gaze-Driven Grasping Action Forecasting" (2024), introduces a novel approach to predicting grasping intentions by analyzing where people look in their environment. This breakthrough has direct applications for individuals with motor disabilities and cognitive impairments, enabling assistive robots to anticipate user needs in real-time. By leveraging human attention patterns, Molina-Moreno's research enhances the safety and responsiveness of robotic systems, making human-robot collaboration more intuitive. His work has already garnered attention in the field, with his key paper accumulating citations that underscore its impact. Through his innovative use of multi-task learning and interpretability, Molina-Moreno is shaping the future of assistive technologies, offering new pathways for independent living and improved quality of life for those with disabilities.
Research Focus
Key Achievements
Top Papers
- 1