I also had the same question, but after looking at this two links: this article and this lecture I think we can say that behavioral cloning (which is the simplest way for doing imitation learning) is just normal supervised learning. But imitation learning is associated with RL because it has other types (improvements) that involve learning while interacting with the environment like Dataset Aggregation, where you have an interactive expert that you can query for optimal actions to label observations obtained by rolling out the learned policy in the environment, and you repeat collecting the data and training the policy. This is done to mitigate the known problem of distributional shift in BC. More about it can be found here

I also had the same question, but after looking at this two links: this article and this lecture I think we can say that behavioral cloning (which is the simplest way for doing imitation learning) is just normal supervised learning. But imitation learning could be associated with RL because it has other types (improvements) that involve learning while interacting with the environment like Dataset Aggregation, where you have an interactive expert that you can query for optimal actions to label observations obtained by rolling out the learned policy in the environment, and you repeat collecting the data and training the policy. This is done to mitigate the known problem of distributional drift in BC. More about it can be found here.

Even with this interactive expert, I still think it is supervised learning, but I can understand why some people might associate it with RL.