OpenVLA is basically a slightly modified, fine-tuned llama2. I found the launch/intro talk by lead author to be quite accessible: https://www.youtube.com/watch?v=-0s0v3q7mBk

1. Well, based on Karpathy's talks on Tesla FSD, his solution is to actually make the training set reflect everything you'd see in reality. The tricky part is that if something occurs 0.0000001% IRL and something else occurs 50% of the time, they both need to make 5% of the training corpus. The thing with multimodal LLMs is that lidar/depth input can just be another input that gets encoded along with everything else, so for driving "there's a blob I don't quite recognize" is still a blob you have to drive around.

2. Figure has a dual-model architecture which makes a lot of sense: A 7B model that does higher-level planning and control and a runs at 8Hz, and a tiny 0.08B model that runs at 200Hz and does the minute control outputs. https://www.figure.ai/news/helix

You can think of these as essentially multi-modal LLMs, which is to say you can have very small/fast ones (SmolVLA - 0.5B params) that are good at specific tasks, and larger/slower more general ones (OpenVLA - a finetuned llama2 7B). So a rpi could be used for some very specific tasks, but even the more general ones could run on beefy consumer hardware.

I've spent the last few months looking into VLAs and I'm convinced that they're gonna be a big deal, ie they very well might be the "chatgpt moment for robotics" that everyone's been anticipating. Multimodal LLMs already have a ton of built-in understanding of images and text, so VLAs are just regular MMLLMs that are fine-tuned to output a specific sequence of instructions that can be fed to a robot.

OpenVLA, which came out last year, is a Llama2 fine tune with extra image encoding that outputs a 7-tuple of integers. The integers are rotation and translation inputs for a robot arm. If you give a vision llama2 a picture of a an apple and a bowl and say "put the apple in the bowl", it already understands apples, bowls, knows the end state should apple in bowl etc. What missing is a series of tuples that will correctly manipulate the arm to do that, and the way they did it is through a large number of short instruction videos.

The neat part is that although everyone is focusing on robot arms manipulating objects at the moment, there's no reason this method can't be applied to any task. Want a smart lawnmower? It already understands "lawn" "mow", "don't destroy toy in path" etc, just needs a finetune on how to corectly operate a lawnmower. Sam Altman made some comments about having self-driving technology recently and I'm certain it's a chat-gpt based VLA. After all, if you give chatgpt a picture of a street, it knows what's a car, pedestrian, etc. It doesn't know how to output the correct turn/go/stop commands, and it does need a great deal of diverse data, but there's no reason why it can't do it. https://www.reddit.com/r/SelfDrivingCars/comments/1le7iq4/sa...

Anyway, super exciting stuff. If I had time, I'd rig a snowblower with a remote control setup, record a bunch of runs and get a VLA to clean my driveway while I sleep.

> creating an unprecedented crisis

Nope, that's just your brain on right wing propaganda. Nothing of the sort has happened to justify throwing away the rule of law as this administration is doing. It is like murdering someone because you're annoyed that they chew with their mouth open, just an absolutely, wildly disproportionate response to what has happened. Coincidentally (to riff on the GP) it is almost word-for-word the propaganda Orban used in the mid 2010s to consolidate his authoritarian rule of Hungary.