In language models, turning a generative model into a critic is simple: pass in the output and prompt for a score. Robot policies can't do this - they output physical actions & there's no built-in vocabulary to predict scores. So when it comes to RL on robot policies, we typically train a separate critic from sparse rewards - blind to everything the policy learned from the actions in the pretraining data. We think this has it backwards - can a critic learn from those actions the way the policy does? (1/n)

Your robot's shape shouldn't limit its sense of touch. Meet OpenTac: an open ecosystem for custom tactile skins that sense normal + shear forces. Very excited about this collaborative project! Can’t wait to see what the community builds with it. 🧵👇 opentac.github.io

[1/7] We trained a world model on 30,000 hours of first-person video. It learned exactly where the hands go. What happens to the things they touch, much less so. Going from 300 to 30,000 hours did not narrow the gap, and a 4× larger model barely did either.

NEVER LOOK BACK! Introducing LEDGER: persistent 3D object memory from egocentric videos. Remember where things were, when they moved, and what they held -> then answer questions without looking back at the video! ledger-3d.github.io

We’re on a mission to build a billion AI models, not trained by us, but by you. Introducing @bryel_labs we help your company build its in-house AI lab and give you the software to make model development and continual improvement simpler. We are backed by @ycombinator and have trained models at Apple, Framer, and F500s. We’ve enabled companies to train: • Classification models that are 20x faster • Search models that cost 5x less. • Long horizon agent models that match the frontier at 5-8x lower cost. Are you interested in partnering with us? Reach out. bryel.ai/

Excited to share that MoSE3 was accepted as #NeurIPS2026 Spotlight! MoSE3 estimates an SE(3) transform at every pixel from monocular video, all in a shared world coordinate frame and in a single forward pass. It captures rigid, articulated, and deformable motion. 1/

A paradox in robot learning: robot policies are spatially dumb and data-inefficient but their image features are spatially robust and semantically rich. Image encoders are spatially smart — features embed semantics + geometry + are multiview-consistent ... but robot policies we build upon them are spatially dumb — hundreds of demos to learn simple pick+place and a small camera bump breaks them To better bridge this gap, check out BIND: a new action head that binds each candidate robot action to its projecting 2D image feature(s) BIND basically offers the network the info of '‘choosing this candidate robot action would move the robot EEF to this image feature." Result: policies much more data efficient and robust to OOD viewpoints and OOD object positions (same rgb input, no 3D sensors, dense EEF trajectories out) 🧵