  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.
