Robot Utility Models (RUMs) enable basic tasks – door... opening, drawer opening, object reorientation, etc. – at ~90% accuracy without ANY finetuning (i.e. zero-shot) in unseen new environments. Fully open source!!! models, data, code & hw. We think this is super exciting, why?👇 1. Unlocks many practical home utility tasks that often involve these basic tasks as part of an action chain. “Go get me a fork” involves opening the kitchen door and then opening the cutlery drawer. 2. This works well **zero-shot in unseen and new** environments, which is practically a huge deal. Turn the robot on, and get going. 3. The recipe for building a new model is fairly generic, and we think with a bit more refinement this can be a general recipe to build many more Utility models. More details and access 👇show more

Qualia has been selected for the Google DeepMind Robotics... Program. We train embodied models that put a robot on a real manual task and make it work, on the floor, not in a demo. Foundation models and reasoning are where robotics is heading, and doing that work alongside DeepMind, who are pushing this frontier, is exactly where we want to be. If you are a company looking to see how a new generation of robots can help your manual tasks, contact us at [email protected] More soonshow more

Robot policies can succeed in a demo and still... fail as tasks, environments, and conditions change. With open-source Isaac Lab-Arena 0.3, developers can use an experimental agentic workflow to generate environments, evaluate policies at scale, and identify where and why failures occur.show more

How can we more effectively leverage robot data from... different embodiments for skill transfer? Excited to share that our new work, RoVi-Aug, has been accepted to Conference on Robot Learning as an oral paper！ WIth RoVi-Aug, you can augment an existing robot dataset into a different robot and different viewpoints. A policy trained on the augmented dataset can zero-shot deploy on the unseen target robot with significantly different camera angles! 🧵👇 🔗 Check out our paper:show more

Predicting how the environment changes over time is a... key task in robotics, enabling all kinds of downstream tasks. Trace Anything is a very interesting look at how you can train models which predict the trajectory each point will take in the future -- you can use this for robot manipulation, video prediction, and more. Learn more ->show more

August came and went in a flash and what... a month it was! We opened 15 new stores (including the opening of our first store in Zimbabwe 🇿🇼) and completed 1 revamp. Things are really ramping up as we head into the final stretch of the year, with more new countries and many more store openings still in the pipeline for 2025. 🚀 The momentum is building, and we’re just getting started!show more

Train a TensorFlow object detection model – then deploy... it on a robot 🤖 Iulia Feroli (Iulia Feroli) shows how to turn a notebook into a real-time object detection app. This tutorial works for any project – though we demonstrate the deployment on (and assisted by!) the #ReachyMini, an open-source robot from Pollen Robotics. Built with PyCharm + Claude Code. 👉 Watch it in action:show more

World Models are the path for some AI Models... in the future. But how can we efficiently train these models to not only see the world the way humans do but to see the world in a new and unique way. By visualizing, what is normally sequenced audio patterns, we can derive much more insights. Here we see Paganini in a visual form that can than be described and transcribed into a World Model. We can observe connections in a manner that may not have been clear prior to the digitalization of music and sound in this way. The company with the most valuable potential in building a World Model is Tesla. Not that this type of visualization is being used, but that the mechanisms are in place, and the technology is in place for the company to thrive in this new form of AI.show more

Opening doors to Headout's new HQ in BLR. A... space we imagined as a Museum of the World, designed as an ode to the big, beautiful world around us. We made it with love, and there’s room for many more. We’re hiring across roles. Come build with us.show more

Is Jev is a game changer for robotics? We... gave Jev a robot body and handed it complex tasks across navigation, spatial reasoning, and world geometry 120 different real + simulated tasks and environments benchmarking performance against Dimcode, Astra, Fable, Opus, and 5.6 We graded against speed, cost, tokens, # collisions, path quality Code, Data, and Paper dropping tomorrow. The results were surprising.show more

🤖 Another zero-shot reward model is now in LeRobot:... ROBOMETER. A general-purpose, zero-shot video-language reward model from University of South Carolina, UT Dallas, Massachusetts Institute of Technology (MIT), University of Washington, Ai2, and NVIDIA that predicts frame-level task progress. Trained on 1M+ trajectories from 21 robot embodiments, generalizes zero-shot to unseen tasks, scenes, and robots. 2.4–4.5x better downstream success rates across online RL, offline RL, data filtering, failure detection, and data retrieval for IL. Project: Paper:show more

The opening of NODE feels special. A real home... for the CryptoPunks and a meaningful step for NFTs as digital art. Less noise, more culture. This is about understanding, preserving, and taking digital art seriously. A new chapter is starting.show more

Proud to announce Dobb·E: the next step in home... robot system that I was working on for the past 3 years. We have visited 10 homes, learned 100+ tasks, and we are just getting started! And we fully open-sourced it all, hardware, models, and software: 🧵show more

Introducing $SAHARA — the native utility token of Sahara... AI. Designed for real-world function, $SAHARA powers a new economic model for AI. One that is collaborative, open, and built to reward real contributors. Utility, distribution, and claim details below 🧵👇show more

Robotics keeps hitting the same wall. Single task RL... works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:show more

What would it look like to see all ~13,000... proteins inside a single human cell at once? We share ProtiCelli — a generative model that makes this possible. Besides benchmarking, we demonstrate utility in many tasks. Preprint: A thread 🧵👇show more

It’s been a verrrrrry rainy opening for our #134th... Karmagawa school but what a great community this is! I LOVE OPENING NEW SCHOOLS LIKE THIS AS THEY ARE LIFE-CHANGING FOR THESE KIDS!!! Retweet and help me get more traders and really all people giving back too! #learningiskeyshow more