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

• URL: http://arxiv.org/abs/2206.10797v1
• Date: Wed, 22 Jun 2022 01:36:14 GMT
• Title: Imitation Learning for Generalizable Self-driving Policy with Sim-to-real Transfer
• Authors: Zolt\'an L\H{o}rincz, M\'arton Szemenyei, R\'obert Moni
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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