However, I don't quite understand how the authors are encoding "domain invariance" with "a domain adversarially invariant meta-learning algorithm." I'm not sure what that means. If any of the authors are on HN, a more concrete explanation of such "domain invariance encoding" would be greatly appreciated!
Finally, I have to say: The field of deep learning and AI is going to benefit enormously from the involvement of more people with strong backgrounds in physics, specially the theorists who have invested many years or decades of their lives thinking about and figuring out how to model complicated physical systems.
Doing the behavior that feedback driven control-systems do but even better is a nice and impressive applications. That seems most useful for applications like the application that's being described - swarms of flying drones. Flying generally already yielded to various control system - autopilots work because the skies are mostly empty and so your system working according to your predictions is all that matters. A drone swarm is much more complicated but is still under the system's control.
It's worth saying that the "real world" where a lot of robots fail has different challenges. Whether you're talking self-driving cars, robot dogs accompanying troops or wheeled delivery robots in hospitals, the problem is figuring both what you're looking at and how to respond to it. And this has the problem that nearly anything can show up and require unique responses, causing progress here to never quite be enough. And better physics and better cooperation between controlled elements doesn't seem that useful here and this approach might not help this "real world".