Peter Stone's Selected Publications

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Recent Advances in Imitation Learning from Observation

Faraz Torabi, Garrett Warnell, and Peter Stone. Recent Advances in Imitation Learning from Observation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), August 2019.

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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.

BibTeX Entry

@InProceedings{IJCAI19a-torabi,
  author = {Faraz Torabi and Garrett Warnell and Peter Stone},
  title = {Recent Advances in Imitation Learning from Observation},
  booktitle = {Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI)},
  location = {Macao, China},
  month = {August},
  year = {2019},
  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.
  },
}

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