论文标题
通过SIM到现实转移的模仿学习可概括的自动驾驶政策
Imitation Learning for Generalizable Self-driving Policy with Sim-to-real Transfer
论文作者
论文摘要
模仿学习使用专家的演示来揭示最佳政策，并且也适用于现实世界的机器人技术任务。但是，在这种情况下，由于安全，经济和时间限制，在模拟环境中对代理进行培训。后来，使用SIM到现实方法将代理应用于现实域。在本文中，我们采用模仿学习方法来解决模拟环境中的机器人技术任务，并使用转移学习将这些解决方案应用于现实环境。我们的任务设置在Duckietown环境中，机器人代理必须根据单个前向摄像头的输入图像遵循右车道。我们提出了三个模仿学习和两种能够完成此任务的模拟方法。在这些技术上提供了详细的比较，以突出它们的优势和缺点。
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.
