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Recent Advances in Imitation Learning from Observation.
Faraz Torabi,
Garrett Warnell, and Peter
Stone.
In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), August
2019.
[PDF]157.4kB [slides.pptx]45.5MB
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.
@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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