Mission

Developing a navigation stack for a robot requires careful design and tuning of several path-planners and control algorithms. On the other hand, deep reinforcement learning methods enable us to learn complex functions without the need for labeled examples. Having a model of the robot, we aim to develop methods that replace classic path-planners and control algorithms, but instead a navigation policy is learned in simulation and deployed on a real robot.