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.
POLSAB aims at advancing the state-of-the-art in safe imitation learning for high-dimensional domains in an end- to-end approach. We focus on two main applications: autonomous robot navigation and self-driving car simulations. In order to design efficient and safe policies (which map observations to actions) for these tasks, it is necessary more than just using behavioral cloning which basically applies supervised learning on a labelled dataset.
Control tasks usually have the issue of cascading errors. This happens when the controller's policy does not take into account the feedback loops of controller mistakes: little deviations from the desired reference track (the street lane, or the robot path) causes the error to feedback into the policy as new observations arise, until no valid action is possible anymore.
In order to fix these problems, in this project, we will use a recently introduced framework called Generative Adversarial Imitation Learning (GAIL) for learning robust policies by imitation learning for the robot and the simulated vehicle. To minimize the risk of high-cost events (accidents), the risk-averse version of GAIL will be extended to our application domains.
A second approach will tackle the recent framework of option-critic in Reinforcement Learning (RL), where by defining a reward function (a qualitative measure of the agent's behavior) it is possible to learn robust control policies by trial and error. This method also takes into account temporal abstractions in the policy mappings by creating hierarchies of behaviors in time, which makes it possible to scale up reinforcement learning.
Finally, this project will contribute to research in safe AI by investigating risk-sensitive methods in applications where this is of paramount importance in order to position Luxembourg as a important player in billion dollars industries: safe, robust AI agents in real-world settings (e.g. trading, autonomous driverless vehicles, service robotics).
SUPPORT (if project is realized with FNR support):
PLATFORMS TO BE USED IN THE PROJECT:
Turtlebot Waffle with camera and LiDAR sensors for autonomous navigation
CARLA: simulation for self-driving/autonomous cars in urban environments
TORCS: simulation for racing/road autonomous cars