Have you ever thought about the problem of learning efficient periodic behaviours for robots (e.g. pick-and-place or flapping)? Well, we have! 

In our most recent work, we used a neural network (in particular a neural ODE) to shape the (close-loop) behaviour of our system such that its natural evolution corresponds to the desired periodic movement. [article]

We will soon release our code too!

A few months ago, I embarked on a quest with the goal of writing a complete and understandable review on the field of "State Representation Learning" for experts and non-experts in the field. State Representation Learning is the problem of recovering low-dimensional and meaningful state representations from high-dimensional data.  Recovering the state vector is crucial for unveiling dynamics and control of robots and dynamical systems.

It was great learning experience (it was my first review paper) and I am really happy with the results! Enjoy the reading! Get ready because it is a long paper ;) [article]

I have also developed and released the code comparing 18 of the different methods on the problem of learning a compact state representation for a pendulum from high-dimensional observations (i.e. RGB images) with and without visual distractors. [code]

What a day! For the 3rd time, I am a finisher of the IRONMAN 70.3 Italy and I scored a new personal best (pb) too! 

It only ;) took me 4:49:03h to complete 1.9km of swimming in the sea, 90km of cycling, and 21.1km running for a total of ~113km (70.3miles)! 

So happy to have completed my first work as a postdoc! 

In our work [here], we study the problem on dimensionality reduction, model-order reduction, and uncertainty quantification for dynamical systems from high-dimensional and noisy measurements! All these problems can be tackled jointly with a smart combination of deep neural networks and gaussian processes, i.e. the deep kernel learning. 

We have released our code too! [code]

The first race of the season is always the toughest, no matter distance and preparation! However, it was a great fun to push hard again after the winter break :)

After two years, I finally got the chance to physically present my work on "State and Action Representation learning for Reinforcement  Learning" [article][presentation] to a physical audience (and not to my laptop screen)! I was a bit stress to do that after so long, but only after a few seconds, the excitement too over!