The imitation learning research community has recently made significant progress towards the goal of enabling artificial agents to imitate behaviors from video demonstrations alone. However, current state-of-the-art approaches developed for this problem exhibit high sample complexity due, in part, to the high-dimensional nature of video observations. Towards addressing this issue, we introduce here a new algorithm called Visual Generative Adversarial Imitation from Observation using a State Observer VGAIfO-SO. At its core, VGAIfO-SO seeks to address sample inefficiency using a novel, self-supervised state observer, which provides estimates of lower-dimensional proprioceptive state representations from high-dimensional images. We show experimentally in several continuous control environments that VGAIfO-SO is more sample efficient than other IfO algorithms at learning from video-only demonstrations and can sometimes even achieve performance close to the Generative Adversarial Imitation from Observation (GAIfO) algorithm that has privileged access to the demonstrator's proprioceptive state information.

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In International Conference on Robotics and Automation, 2022, Philadelphia, Pennsylvania, May 2022.

@inproceedings{ICRA22-karnan, title={Adversarial Imitation Learning from Video using a State Observer}, author={Haresh Karnan and Garrett Warnell and Faraz Torabi and Peter Stone}, booktitle={International Conference on Robotics and Automation, 2022}, month={May}, address={Philadelphia, Pennsylvania}, url="http://nn.cs.utexas.edu/?ICRA22-karnan", year={2022} }