Incorporating priors from physics into hybrid DNN-blackbox + traditional models makes a lot of sense for these kinds of applications. It also makes sense that regularizing the DNN blackbox to make sure it's "smooth enough" (i.e., ensuring the change in output in relation to the change in input stays below some threshold) helps make these complicated models more stable.

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.

This depends on which "real world" you're talking about.

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".

I've been watching Steve Brunton's lab closely on discovering dynamical/control systems via NN's/Auto-Encoders. His videos really helped me figure out what was happening in the background to figure out sparse solutions to chaotic systems: https://www.youtube.com/watch?v=KmQkDgu-Qp0
What bugs me about most sci-fi is that the robots have bad aim. Watching these neural control videos, it becomes pretty clear that the robots will kill the people in a fictional sci-fi setting from miles away before our protagonist even knows they're there.
Wasn't Andrew Ng doing this since 2008?


Interesting part about taking advantage of invariances. There is more to this article than what I can digest on Thanksgiving. Book marked for later.
IIRC the most advanced robots today, those by Boston Dynamics, do not use any deep- or machine learning. Or at least they did not when I'd last read about them. Tech simply did not exist when they were developed. So this is a good stake in the ground in a relatively nascent (but "difficult" field). Hopefully this will result in more innovation, although this is _fundamentally_ difficult as well because what the neural models will do given out of domain inputs is anyone's guess, and that can end pretty badly in a control system. But this still could be helpful for augmenting traditional control systems, which tend to be relativel primitive, and tend to make a lot of assumptions, such as linearity.