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

本文研究应用 Imitation Learning 和 transfer learning 方法解决 Duckietown 场景下的机器人车道跟踪问题，并通过 sim-to-real 方法进行实际环境迁移，最终比较了三种 Imitation Learning 方法和两种 sim-to-real 方法的优缺点。