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Imitation Learning uses the demonstrations of an expert to uncover theoptimal policy and it is suitable for real-world robotics tasks as well. Inthis case, however, the training of the agent is carried out in a simulationenvironment due to safety, economic and time constraints. Later, the agent isapplied in the real-life domain using sim-to-real methods. In this paper, weapply Imitation Learning methods that solve a robotics task in a simulatedenvironment and use transfer learning to apply these solutions in thereal-world environment. Our task is set in the Duckietown environment, wherethe robotic agent has to follow the right lane based on the input images of asingle forward-facing camera. We present three Imitation Learning and twosim-to-real methods capable of achieving this task. A detailed comparison isprovided on these techniques to highlight their advantages and disadvantages.

Imitation Learning for Generalizable Self-driving Policy with Sim-to-real Transfer