Imitation learning is the process by which one agent tries to learn how to perform a certain task using information generated by another, often more-expert agent performing that same task.Conventionally, the imitator has access to both state and action information generated by an expert performing the task (e.g., the expert may provide a kinesthetic demonstration of object placement using a robotic arm). However, requiring the action information prevents imitation learning from a large number of existing valuable learning resources such as online videos of humans performing tasks. To overcome this issue, the specific problem of imitation from observation (IfO) has recently garnered a great deal of attention, in which the imitator only has access to the state information (e.g., video frames) generated by the expert. In this paper, we provide a literature review of methods developed for IfO, and then point out some open research problems and potential future work.

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Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI) (2019).

@article{IJCAI19a-torabi, title={Recent Advances in Imitation Learning from Observation}, author={Faraz Torabi and Garrett Warnell and Peter Stone}, booktitle={Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI)}, month={August}, address={Macao, China}, url="http://www.cs.utexas.edu/users/ai-lab?IJCAI19a-torabi", year={2019} }