This is 100% not true for computer science and I don't know why you think it's true. There's are entire branches of computer science (e.g. complexity theory) that are highly theoretical and are certainly not geared towards immediate practical application.
Even in more applied branches of computer science like ML there are many papers that are geared more towards the fundamental research side of things.
> saying that it aims to "learn a simulator [for robotic agents] by simply watching an agent interact with an environment".
Saying that a paper could lead to a useful application down the line is not the same that saying that the paper is intended to describe the schematics for a specific useful application.
I'm not sure who brought up a schematic? The bit I quoted is taken from the paper and it's a clear attempt to motivate the paper's approach as having a practical application. To clarify, do you disagree with that?
Indeed, a common criticism against papers in machine learning is "what is a real-world application of this approach?". If you can't answer that convincingly then it's unlikely your paper gets published, particularly if it has rather mediocre results otherwise, as is the case with this paper (see my earlier comment about the comparison to the two other systems which is a bit of an anticlimax).
In fact, the paper (well, the preprint version) makes a clear attempt to motivate the work with a practical application, saying that it aims to "learn a simulator [for robotic agents] by simply watching an agent interact with an environment".