Imitation Learning for Generalizable Self-driving Policy with Sim-to-real Transfer
This work addresses the challenge of safe and efficient policy deployment for robotics, specifically in autonomous driving, but it is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of training a self-driving policy in simulation and transferring it to the real world using imitation learning, achieving successful lane-following in the Duckietown environment with a comparison of three imitation learning and two sim-to-real methods.
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.