Imitation Learning: Progress, Taxonomies and Challenges

Understand

Imitation learning aims to extract knowledge from human experts' demonstrations or artificially created agents in order to replicate their behaviors.

• Its success has been demonstrated in areas such as video games, autonomous driving, robotic simulations and object manipulation.
• However, this replicating process could be problematic, such as the performance is highly dependent on the demonstration quality, and most trained agents are limited to perform well in task-specific environments.
• In this survey, we provide a systematic review on imitation learning.