Abstract
Imitation learning is the process by which one
agent tries to learn how to perform a certain task using information generated by another, often moreexpert 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