Carlos Rubert was born in 1987 in Castellón de la Plana. He has a degree in B.S. in Computer Science at Universitat Jaume I (September 2012), he also coursed a year of Computer Science (2009-2010) at Universitat Degli Studi di Milano (Erasmus Programme). Also has a M.Sc in Intelligent Systems (2013) at Universitat Jaume I. He started his PhD in Computer Science at Universitat Jaume I in 2013. Nowadays is a guest researcher at the MPI.
His work is focused in the use of Quality Metrics for evaluating grasps and robotic hands. He also working in the development of prosthetic hands for amputees.
In Proceedings of the IEEE/RSJ International Conference of Intelligent Robots and Systems, September 2017 (inproceedings) Accepted
We aim to reliably predict whether a grasp on a known object is successful before it is executed in the real world. There is an entire suite of grasp metrics that has already been developed which rely on precisely known contact points between object and hand. However, it remains unclear whether and how they may be combined into a general purpose grasp stability predictor. In this paper, we analyze these questions by leveraging a large scale database of simulated grasps on a wide variety of objects. For each grasp, we compute the value of seven metrics. Each grasp is annotated by human subjects with ground truth stability labels. Given this data set, we train several classification methods to find out whether there is some underlying, non-trivial structure in the data that is difficult to model manually but can be learned. Quantitative and qualitative results show the complexity of the prediction problem. We found that a good prediction performance critically depends on using a combination of metrics as input features. Furthermore, non-parametric and non-linear classifiers best capture the structure in the data.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems