The research is focused primarily on developing a clear understanding of the computational and engineering aspects related to perception, reconstruction, recognition and tracking with the aim of improving existing robotic systems and/or designing new models to allow for a semi-autonomous or fully autonomous operation.
This research is related with the discovery of hidden relationships in data containing a large amount of variables in comparison with a small number of observations. This falls in the field of statistical analysis with an emphasis on dimensionality reduction for building a more suitable representation for the data. Besides other applications such as humanoid robotics, acoustics and more recently agricultural analysis, we have applied dimensionality reduction techniques in two main areas of computer vision: Shape Analysis and 3D reconstruction.
The main topics here are related with object recognition and image understanding applied to mobile and aerial robots. We explore Machine Learning Techniques to solve real world problems.
Motion generation and control for anthropomorphic mechanisms
Humanoid robots
Virtual mannequins
Redundant arms and hands
Vision-based robot localization and navigation
Computational models of human walking
Differential systems
Statistical models
