Compliant Mechanisms that LEARN! - Mechanical Neural Network Architected Materials

at 11 minutes I realised this was a research paper in an easily digestable and widely available format, great work.

This is an incredible combination of an entertaining youtube video and a technical paper presentation! I wish more articles were presented like this

As a mechanical engineer who is learning computer science and machine learning, this is an amazing bridge between the two worlds! I cant wait to print some and play with the concept myself. The applications are truly endless, I wonder how long until this is made microscopically, and applied everywhere.

My heart goes out to the graduate students who did all this work. You guys are ferocious, you deserve only the best in life.

I went from complete "what is this I don't even" to "okay this makes sense, cool" in 20 minutes. Very well presented, super interesting and understandable even to someone with zero experience in mechanical engineering.

In a general sense this is what bone and connective tissues do. They have built-in stress sensors that look for electrical signals that appear in weak spots in the bone and connective tissue. They rebuild the structure to fix those weak spot s and redistribute load.

This is insane. Soon we'll be doing this kind of stuff with photolithography. Perhaps it'll be the next step in neural networks as a whole to increase efficiency.

I wonder if you could use plant cells to do something like this. Have a gas-filled vacuole inflate/deflate across a uniform foam of cells, which alters the tension against the cell walls, allowing for control over the material stiffness. plants already do this naturally to grow twards light, but imagine it being used as an organic wing. I imagine it would be made up of something like cactus flesh, filled with a microfluidic network to control local stiffness.

I guess after trying the dynamic learning you could (mass) produce a hard-coded version with the same values and just 3D printing :)

Materials Science and Engineering dropout here. I couldn’t hack it in academia at that level, I had the smarts but it was too much stress and pressure. But I still love the subject matter, I think it’s absolutely fascinating, and stuff like this video is what sent me into that field in the first place. Thank you for the detailed breakdown, this was awesome to watch.

This is amazing. Miniaturized / nanoscale applications of this really could drive world-changing metamaterial developments. It's also a very helpful way to unpack and visualize the fairly opaque world of learning neural networks in general. I never mind waiting for content like this. Thanks so much for the walkthrough!

Absolutely fantastic! I look forward to seeing this get progressively miniaturised.

I thought the whole point was it's a neural net where the weights have a physical meaning (i.e. the displacement), but you don't represent it that way or use gradient descent to optimize the weights. The main benefit of neural networks is that they are differentiable and so can be efficiently trained with gradient descent.

Very interesting! Though it feels like there will be a lot of problems with miniaturising this type of system. My intuition tells me that most miniature things wouldn't be tunable by the connections between nodes, but rather the nodes themselves. For example I could imagine a theoretical case where each node has some sort of "pressure" that it applies universilly to all of its neighbors. It may even be as simple as laying out a latice of beads either of different materials, or hollow with different air pressures or wall thicknesses. Thus, what I would be most interested in seeing next is simulating a node-pressure centric model, to see if changing the adjustable factors from the beams to them would still be able to produce the behaviours that were exhibited in this video.

You should be able to manufacture a much cheaper and easier to scale version of this by using electro-osmotic cells (cellulose membrane tube with internal electrode between two plates is probably the simplest) as the stiffness altering actuator. Simply increase the voltage on the cell to increase the internal pressure.

This is fantastic way to present your paper. Very interesting research, I am looking forward to more work from your lab!

I feel like having a lattice of adjustable stiffness beams is the much, much more impressive feat than the neural network part. Like, does such a lattice exist in usable ways (ie building or clothing scale devices)?

this is one of those things that is first off awesome to share with the world in this format (no way in hell i would have ever read the paper) and also an extremely interesting idea with genuine potential to change the world, thank you so much for taking the time to make such an entertaining and informative video!

This is an idea that I had I wanted to share with yall. This idea has been partially implemented in the video but I want to extend it. What if instead of optimizing the model in the real world you created a computer simulation that would give you more accurate results and a much faster interface (because it's software <-> software instead of software <-> real world). Now that you are doing the simulation part purely digitally you don't really need such a complicated mechanism to vary the stiffness. Instead, you could export the result of the computer simulation in a format readable by 3D printers. Instead of your current mechanism, you could have something like a coil that could be stiffness-manipulated by varying its width. Now, yes, this is a much less "dynamic" approach because it does not allow you to change the values on-the-fly and requires you to 3D print your material every time you want to test it in the real world but as long as your Simulation -> Real World process is accurate enough you should not need to 3D print your material every time you want to test it and should be able to do it using only software and only need to 3D print it when you want to be absolutely sure that the material behaves as it should.

Thanks for this, this is super cool! It's given me inspiration for a potential project of my own, albeit much lower budget and tech. Consider a PLA 3D printed lattice in a similar configuration as the triangular one you used here, but using a slight curve on the beams to allow them to bend. Consider then pressing the lattice into a mold with a force, to the point of plastic deformation. The plastic deformation of the compliant mechanisms - the damage the beams suffer - could serve as a kind of learning process, reducing the weights of certain beams, and increasing the strain and thus weights on others. Setting this apart from traditional machine learning - aside from the medium - is that the training is not easily reversible; the plastic deformation can't be undone, and for a weakened beam to become relevant again can only happen within the context of other interacting beams becoming relatively weaker too. Thus, I don't think it could learn many behaviours, as the system is essentially lossy. I'm not aware of any literature that tests neural networks whose weights can only ever shift in one direction; they would naturally be less accurate, and you would have to take a very slow and conservative learning approach so as not to totally collapse the system, but I would be very interested to see how it goes. Perhaps I'll start off with that kind of computational model. I'm also not sure how effectively it would work with simply molding it to shape, as it could use the mold as a crutch with different output forces on different locations, resulting in a different shape when not constrained by the mold. Perhaps rather than a primitive mold, then, a rig of fource gauges at the output locations could be there and seek to find where the output force is zero at the desired location; if the material is overpressuring a certain output location, you can apply a counteractive force at JUST that output location to create plastic deformation until said output force IS zero. This would have to be done conservatively and stepwise, as reducing the error at that location will inevitably create more error across the other locations; the maximal error output would have to be tweaked slightly, then the next, etc. Would love to know your thoughts, and thanks again! :)