Our paper, resulting from work of Master student Gustavo Claudio Karl Couto, entitled
"Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban Environments" has shown very interesting results:
Both the intermediate mid-level input BEV representation and the control policy are learned as the agent navigates in an urban town (click for more videos from playlist).
State-of-the-art Artificial Intelligence method for detecting that you is really you and not some intruder entering the code on your mobile phone.
During my PhD, I've worked mainly on Reservoir Computing (RC) architectures with application to modeling cognitive capabilities for mobile robots from sensor data and sometimes through interaction with the environment.
Reservoir Computing (RC) is an efficient method for trainning recurrent neural networks, which can handle spatio-temporal processing tasks, such as speech recognition. These networks are also biological plausible, as recently argued in the literature.
In my case, I used these RC networks for modeling a wide range of capabilities for mobile robots, such as:
These tasks were modeled basically using regression for learning behaviors or classification for discrete localization.
My PhD thesis can be download here. It is entitled: "Reservoir Computing Architectures for Modeling Robot Navigation Systems".
My publications are listed and can be downloaded in Google Scholar or here.
Some simulated and real robots employed in the experiments:
Environment used for localization experiments using the real e-puck robot:
It is possible to perform map generation through sensory prediction given the robot position as input. Black points represent the sensory readings whereas gray points are the robot trajectory.
