All hail RT-X: The global project aimed at creating an AI mega brain for all robotkind
Like it or not, we're very much in the world of generative AI now. Massively complex neural networks trained on vast quantities of data, all so we can use it to make pictures of donkeys riding space rockets or tell us which churro coating is the best. I jest, of course, because large language models (LLMs) can be very useful but there's one area they've yet to be used in and that's robotics. Not anymore, as Google, the University of California, and a host of other labs around the world have started the RT-X project, with the aim of using AI to make an all-purpose 'brain' for robots.
Up to now, nobody seems to have really attempted this but it's only because the data used to train neural networks is based almost entirely on human endeavours, such as art, music, writing, and so on. As shocking as this may seem, the internet isn't full of data about robots and how well they carry out specific tasks.
Hence why Google and the University of California decided to set up the RT-X project (via Fudzilla), roping in 32 other robotics laboratories from around the world, to help them generate the kind of data required to train a neural network. That means collating data from millions and millions of robot interactions, doing such things as pick-and-place or welding in manufacturing lines.
The goal is to have a big enough dataset to create an LLM that can be used to produce the code required to program a robot to do any task. In essence, it's a general-purpose robot brain.
My own experiences of programming robot arms, from the days when I taught engineering, were primitive affairs, but I can easily see the appeal and potential of this work. Rather than manually coding everything yourself, the idea is that you'd type into the interface something along the lines of 'Put oranges in the grey box and leave apples alone.' The LLM would then handle the production of code required to do this.
By using specific inputs, such as a video feed from the robot's camera, the code would be
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