{"type":"ScholarlyArticle","title":"Recent Advances in Imitation Learning from Observation","authors":[{"name":"Faraz Torabi","slug":"faraz-torabi","given":"Faraz","family":"Torabi","affiliation":"The University of Texas at Austin"},{"name":"Garrett Warnell","slug":"garrett-warnell","given":"Garrett","family":"Warnell","affiliation":"Army Research Laboratory"},{"name":"Peter Stone","slug":"peter-stone","given":"Peter","family":"Stone","affiliation":"The University of Texas at Austin"}],"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 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.","doi":"10.24963/ijcai.2019/882","url":"https://ijcai.org/proceedings/2019/882","pdf":"https://ijcai.org/proceedings/2019/0882.pdf","markdown":"https://ijcai.org/proceedings/2019/882.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/882","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Survey track","track":"Survey track","number":882,"pages":{"first":6325,"last":6331},"keywords":["Robotics: Behavior and Control","Robotics: Robotics and Vision","Machine Learning: Reinforcement Learning"],"videos":[],"references":[{"position":1,"raw":"[Abbeel and Ng, 2004] Pieter Abbeel and Andrew Y Ng. 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