Roboticists discover alternative physics
This reminded me a great deal of a paper I read like 10 years ago, which I just looked up because I wanted to see if it was cited here, &quot;Distilling Free-Form Natural Laws from Experimental Data&quot; [0].  Lo and behold it is not only cited, but is written by the same authors.  So the novelty here, as discussed in the paper [1], is that they are doing something similar but from video instead of from sensor streams.  Which is quite interesting, as it opens up the available information to systems that are hard to &#x27;sense&#x27; apart from pointing a camera at them, like the lava lamp example.<p>I did see a presentation a few years ago on a similar topic by Erwin Couwans, author of Bullet physics engine, who was discussing doing neural inference of physics.  He basically wanted to replace Bullet with a neural network, which I thought was kind of funny at the time, but cool if it worked.  (Physical laws at this level of mechanics are mostly quite well understood, but the devil is in the details and actually solving time-discretized non-smooth dynamics, as well as performing good contact detection is less than obvious the more you get into it, not to mention friction, so I could see why a &quot;learned&quot; solution could be attractive, but at the time I found it funny that he was proposing to learn the physics that were already simulated by Bullet.)   Looking back, it appears that those ideas culminated in a paper [2] two years ago, which I&#x27;ll have to read now -- from the abstract, the differentiability of the simulation has benefits not only for learning the physics (mostly friction models it appears), but for learning controllers for plants within the simulated physics.  It seems to be not cited in the linked article but maybe it is a slightly different, but related, topic.  In any case, fascinating stuff.<p>[0]: <a href="https:&#x2F;&#x2F;www.science.org&#x2F;doi&#x2F;10.1126&#x2F;science.1165893" rel="nofollow">https:&#x2F;&#x2F;www.science.org&#x2F;doi&#x2F;10.1126&#x2F;science.1165893</a><p>[1]: <a href="https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;2112.10755" rel="nofollow">https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;2112.10755</a><p>[2]: <a href="https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;2011.04217" rel="nofollow">https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;2011.04217</a>