Over the last few years, end-to-end robot learning has moved incredibly fast. Even working at the frontier, I’m regularly surprised by how much it can already do. And yet we still haven’t seen it deployed at scale. At @enact_intel, we think that’s because no one has fully committed to this new paradigm yet. Most approaches fall into one of two camps: Humanoid companies start with the form factor. The body comes first, built to look like us, and the AI gets adapted to hardware designed before the model existed. Pure model companies run on off-the-shelf hardware. Fast and convenient, but it limits the modalities and the quality of the data. We see it differently: the body shapes the data, and the data shapes the model. So we’re rebuilding the whole stack: model architectures for multi-frequency, multi-scale intelligence, the data pipelines, and all the way down to the actuators and sensors feeding the model. Our robots sense force and touch through their own actuators, so they feel what’s happening in real time. That makes learning more data-efficient while pushing reliability and performance. It works across form factors and is built for real-world deployment from day one. Huge thanks to everyone who’s helped along the way: early supporters, everyone who took time to give advice, our angels, and the fantastic team I get to tinker on robots with late into the night. @redalpine @firstminutecap @Notion_Cap @ylecun @AndrewLonsberry @arthurmensch @Thom_Wolf @mapo1 @HaraldNieder @spencer_crawley @ascribblenau7 and many more...