Converting video models ➡️ ego-centric world models capable of... humanoids manipulating objects, robots navigating through paintings, and beyond! Huge effort led by Anurag Bagchi over a year! Special thanks to 1X for releasing so much data!show more

Humanoid's humanoid robots are undergoing training at Schaeffler, Siemens,... and Ford factories. By 2026, the value of humanoid robots will no longer be judged by data or models, but by their real-world reliability and usefulness at the customer's site.show more

(1/6) X-Humanoid 🤖: Scaling up data for Humanoid Robots.... We convert human daily activity videos (from Ego-Exo4D) into humanoid videos (i.e., Tesla Optimus) performing tasks like cooking or fixing a bike. This data can be potentially used to train robot policies and world models. 🔥 Project page: Paper link:show more

NEW RELEASE: Today we're releasing CortexMAE: a family of... fMRI foundation models trained on 2.1K hours of open fMRI data. We're also releasing Brainmarks: an open benchmark suite for evaluating fMRI foundation models. Full paper is on arXiv (accepted to ICML 2026) A thread:show more

Grok models are now available on Databricks Agent Bricks.... Bring SpaceXAI's latest models to your enterprise data to power capable AI agents.show more

🚀 Excited to share our #ICLR2025 work on planning... with neural dynamics models! While our lab has developed diverse neural dynamics models for manipulating rigid, deformable, and granular objects, having the model alone doesn’t solve the problem—planning with it remains a challenge. 💡 Enter BaB-ND, led by Keyi and Jiangwei! We propose a scalable, GPU-accelerated branch-and-bound algorithm, inspired by neural network verification, to enable effective planning for diverse objects modeled with neural dynamics. 🔗 Project page (open-source + detailed docs!): 🎥 Watch the video to see T being pushed around obstacles, and check out Keyi’s thread for more details!show more

Placing objects sounds simple… until robots have to do... it. This method makes it simple, fast & reliable. [Github ⬇️] Robotic object placement is tough, especially with stacking, hanging, or insertion. AnyPlace is a new two-stage method that uses only synthetic data and a vision-language model to teach robots where and how to place objects; even in the real world. Why this works ✅ Finds the right spot with help from vision-language models ✅ Handles stacking, insertion, and hanging with no real-world training ✅ Trained on synthetic data using Blender and IsaacSim ✅ Works in the real world without fine-tuning It shows that smart use of simulation and language models can make robotic placement tasks easier, faster, and more reliable. Github: Paper: Thank you for sharing Animesh Garg !show more

Latest models and animations for STEAL THE BRAINROT 🔥... Huge thanks to the GOAT FeRinS for the opportunity! #UEFNshow more

This pack of 3D models is releasing tomorrow! Comes... with walls, objects, animated characters, wheelchair, animals, vehicles and more. Guess the price? 👀show more

These models will learn from a huge set of... extremely diverse data from the Tesla fleetshow more

Here's a video of Bertie running, and a few... other photos worth sharing. Thanks for reading through this thread, I want to do more of this where I explain all the work that goes into these models.show more

(1/n) 🚀 With FastVideo, you can now generate a... 5-second video in 5 seconds on a single H200 GPU! Introducing FastWan series, a family of fast video generation models trained via a new recipe we term as “sparse distillation”, to speed up video denoising time by 70X! 🖥️ Live demo: (Thanks to @gmicloud for the support!) 🔗 Blog: 🔓 We fully open-source our models, code, and data with Apache-2.0 licensesshow more

How can robots reliably place objects in diverse real-world... tasks? 🤖🔍 Placement is tough—objects vary in shape and placement modes (such as stacking, hanging, and insertion), making it a challenging problem. We introduce AnyPlace, a two-stage method trained purely on synthetic data to predict diverse placement poses of unseen objects for real-world tasks. Read on for more👇show more

Some random thoughts reading through the new RobbyAnt VLA... paper: - 20,000 hours of data across 9 robots!! - damn, Chinese companies are going to trivially outscale the American ones on real robot data - It's really cool they train on depth; it means they can handle transparent objects really well for example - You can never tell how good these models are without trying them, since everyone trains on different robots, but from the results they show it does seem to clean up - cross embodiment scaling laws are really cool - if you have lots of robots do you need human video??show more

So our 45-person team developed entirely new AI capabilities,... enabling them to: 🎨 Fine-tune custom Veo and Imagen models on their paintings and artwork 📹 Provide a desired look through rough animations, which the models transformed into stylized videos 🎭 Edit specific regions without regenerating entire shots from scratchshow more

Artificial Intelligence (#AI) models that process and interpret visual... data are called large vision models. These models, like large language models, are trained on large amounts of data. #midjourney #dalle #stablediffusion (1/8) 🧵 ⤵️show more

Robots are learning from data like this, at massive... scale. Better models start with better data. Better data needs humans scoring it. Coming soon.show more

With so many AI models out there, which ones... will last? Check out this 17-second clip to see how Grass’s Live Context Retrieval (LCR) engine can give models a unique edge through access to real-time, unbiased web data.show more

📢 Our lab has been exploring 3D world models... for years — and we’re thrilled to share **PhysTwin**: a milestone that reconstructs object appearance, geometry, and dynamics from just a few seconds of interaction! Led by the amazing Hanxiao Jiang 👉 PhysTwin combines **Gaussian splatting** with **inverse dynamics optimization** based on simple **spring-mass** systems. ⚙️ The result? Real-time, action-conditioned 3D video prediction under novel interactions (i.e., 3D world models). 🔑 A few key takeaways: 1. Having the right structure (e.g., particles/masses) helps navigate the trade-off between sample efficiency, generalization, and broad applicability. 2. Visual foundation models (VFMs) have matured to the point where they can provide rich supervision for world modeling (e.g., tracking, shape completion). 3. Beyond VFMs, many crucial components have come together in recent years: Gaussian splats for rendering, NVIDIA Warp for high-performance simulation, and scene/asset generation from a wide range of labs and companies. The future of 3D world models is looking bright! ✨ 4. The resulting digital twin supports a wide range of downstream applications—especially in data generation and policy evaluation, thanks to its realistic rendering and simulation capabilities. 🎥 All code and data to reproduce the results, along with interactive demos, are available on the website. Check the following visualizations of: (1) observations, (2) reconstructed state/actions, (3) interactive digital twins, and (4) the overlays between real-world robot teleoperation and our model’s open-loop predictions.show more

happy Good Friday.. can we get more underwear models... like this all over the world? not asking for much reallyshow more

Thanks for the honor Afrimma ! Thanks to my... beautiful fans! I appreciate you guys so much. Iv got something special up next for y’all ! Ghana 🇬🇭 to the world 🫶🏾show more