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

Autor:	Lőrincz, Zoltán, Szemenyei, Márton, Moni, Róbert
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by ICLR 2022 Workshop on Generalizable Policy Learning in Physical World. Source code is available at: https://github.com/lzoltan35/duckietown_imitation_learning
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2206.10797 Zobrazit plný text záznamu View this record from Arxiv