Recent Advances in Imitation Learning from Observation

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.

Authors

• Faraz Torabi
• Garrett Warnell
• Peter Stone University of Texas at Austin & Sony AI

Keywords

• Machine Learning: Reinforcement Learning
• Robotics: Behavior and Control
• Robotics: Robotics and Vision