Noelia Fernandez
Universidad Rey Juan Carlos, Universidad Carlos III de Madrid
Papers
2
Total Citations
12
H-Index
2
About
Noelia Fernandez is a rising robotics researcher whose work focuses on the critical intersection of humanoid robot control and learning from demonstration. Her most impactful contribution, "Benchmarking Dynamic Balancing Controllers for Humanoid Robots" (2022, 7 citations), provides the field with a rigorous, reproducible comparison of three CoM stabilization and posture adjustment approaches—a foundational resource for researchers developing more stable bipedal locomotion. Building on this, her more recent work, "f-Divergence Optimization for Task-Parameterized Learning from Demonstrations Algorithm" (2024, 5 citations), tackles the pressing challenge of enabling robots to generalize from sparse user demonstrations in unstructured environments. By leveraging f-divergence optimization, Fernandez is pushing the boundaries of how robots can extrapolate skills from limited data, a key step toward more adaptable and autonomous systems. Her dual focus on benchmarking and algorithmic innovation marks her as a thoughtful contributor to both the practical evaluation and theoretical advancement of robot learning and control.
Research Focus
Key Achievements
Top Papers
- 1Benchmarking Dynamic Balancing Controllers for Humanoid Robots7 citations · 2022
- 2