Introducing Flora, an open model suite for scaling robotics data engines. It watches robot and human video and annotate what happened (timestamped instructions) and how well it went (rewards and failure reasons). 🔗 research.nvidia.com/labs/gea… We use it to train NVIDIA GR00T. 🧵

ASPIRE is now open-source! Since our initial release, we've made exciting progress: 🤖 Real-world robot manipulation with GPT-6 Astra. 🔄 Continual skill refinement on physical robots. 📈 Saturated simulation benchmarks: 99% on LIBERO-Pro (80 tasks), 100% on Robosuite (7 representative tasks), and 100% on BEHAVIOR-1K (2 representative tasks). Code: github.com/NVlabs/ASPIRE Project: research.nvidia.com/labs/gea… 🧵 More results in the thread below.

Sharing a copy of my ICRA'26 keynote talk from three months ago. 3 key messages: 1. Building a human-like machine, a dream that has fascinated humankind for centuries, is rapidly becoming a reality. 2. Human data is, by far, the most scalable source of training data to fuel robot foundation models. 3. World models are a "data sponge" that can absorb massive amounts of multimodal data for policy generalization. Video: piped.video/watch?v=cNU_8t7i… Slides: yukezhu.me/talks/building_ge… The talk brought together our work over the past year: GR00T, EgoScale, SONIC, WAM, and more. Here’s the same demo I showed on stage: Our (now old!) model learning to assemble the YCB airplane with zero teleop data. We've made incredible progress since then. What's coming next? 🚀

CoRL 2026 paper decisions are out — check them out on OpenReview! As General Chair, I'm delighted to welcome you to #CoRL2026 in Austin, TX, this November. We expect tickets to sell out quickly, so please register early! corl.org/attending/registrat…

SimFoundry is now open source. Explore it here: github.com/NVlabs/SimFoundry Give it a try and tell us what you think!

SONIC is officially published in Science Robotics today and it made the Science front page. We show the promise of scaling motion tracking toward natural, robust whole-body control for humanoid robots. Huge thanks to the team, and more exciting work is on the way. Paper: science.org/doi/10.1126/scir… Code: nvlabs.github.io/GEAR-SONIC

Proud to be building open robotics models @NVIDIA. Open research FTW!

📢 CoRL early-career keynote nominations are due July 30. Early-career researchers: we encourage reaching out to someone who knows your work well and asking whether they’d consider nominating you. ➡️Nominations: CoRL website.

Success rate has long been the primary metric for evaluating robot manipulation. What about speed? Today, we introduce ⚡️B-spline Policy (BSP). Instead of predicting discrete fixed-rate action chunks, we parameterize actions as continuous B-spline curves. Together with our system design, BSP enables fast manipulation on low-cost robot arms. This project is co-led by @xshenhan, check out his following threads for more details. 🧵 PS: one of my favorite parts of this project was the first time we saw the robots move significantly faster and smoother than the baselines. The videos below are all real time. 👇

We scaled a robot model natively to 8,000 timesteps of context, 5 minutes worth of muscle memory, with constant inference cost. Robot policies used to live their lives a few frames at a time (< 0.1 sec), instantly forgetting what just happened. We pushed to 3 orders of magnitude beyond SOTA. Introducing RoboTTT. Test-Time Training (“TTT”) carries a tiny model *inside* the model. Every incoming sensor reading triggers one gradient step on that tiny core, so the history keeps getting compressed into its weights. The hidden state has a fixed size (literally a small neural net), so the robot can “grok” arbitrarily long experience with little overhead. Learning continues indefinitely after deployment. We can then put an entire video in context as prompt! RoboTTT enables one-shot in-context learning from human video: in circuit board assembly, a human demonstrates a never-seen configuration once, and the robot imitates it faithfully. Humans drop things all the time, but we pick them up so fast that we don’t even notice. That reflex to fix is half of our physical competence. RoboTTT shows self-improvement on the fly: the robot is skilled at recovering from its own errors mid-episode, and each fix enters its context to inform the next move. The TTT core distills a general-purpose, failure-to-correction mapping from the training data. One more thing. What excites me the most is a new Context Scaling Curve: from 128 to 8K timesteps, closed-loop performance hill-climbs steadily with no sign of saturation. 8K-context pretraining beats 1K by 62%. What LLM enjoys, robotics should too. Soon, even 1M context is not a fantasy. Deep dive in thread:

Today, we're introducing SimFoundry, our real2sim2real framework at NVIDIA GEAR that automatically turns real-world scenes into simulation-ready worlds from a single image or video. Website: research.nvidia.com/labs/gea… Paper: arxiv.org/abs/2606.28276 This work marks a major step for our team toward leveraging simulations and synthetic data for foundation model training and systematic policy evaluation at scale. Code will be open-sourced soon. Stay tuned!

How can robots learn dexterous manipulation from human demonstrations at scale? Excited to share CHORD: Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration. CHORD learns from human demos by focusing not only on where contact happens, but how that contact moves the object through force and torque guidance. This unified contact-wrench representation carries human manipulation skills across diverse behaviors, long-horizon tasks, whole-body embodiments, and real-world hardware. We evaluated CHORD on large-scale, long-horizon, contact-rich tasks paired with human demonstrations, spanning rigid, articulated, and multi-object manipulation. At scale: * 82.12% average success across 1,831 tasks * 90.77% whole-body manipulation success * 4,739 sim-ready dexterous manipulation benchmark * Transfer to real dexterous hands Project page: nvidia-isaac.github.io/video… Tech report: nvidia-isaac.github.io/video… Code will be released soon as part of Video to Data repo github.com/nvidia-isaac/vide…, our end-to-end pipeline for converting human demonstration videos into simulation-ready assets and physics-grounded robot training data. Huge thanks to amazing contributors: @zhu_xinghao , Zixi Liu, Shalin Jain, Chenran Li, Milad Noori, Huihua Zhao, John Welsh, @michaelv03, Wei Liu, @TingwuWang , Xingye (Dennis) Da, @zhengyiluo, Vishal Kulkarni, @sNaema, @yukez, @DrJimFan, @bowenwen_me, @danfei_xu, @SohaPouya, @Dr_YanChang. #Robotics #PhysicalAI #DexterousManipulation #RobotLearning #NVIDIA

Excited to share T-Rex: Tactile-Reactive Dexterous Manipulation 🦖🤖 Touch is fundamental to human dexterity, yet most Vision-Language-Action (VLA) models either ignore tactile feedback or lack the ability to react to high-frequency contact signals. In this work, we tackle both the data and architectural challenges of tactile-reactive dexterous manipulation. 🦖 A 100-hour tactile-synchronized dexterous manipulation dataset with 7,700+ trajectories, 22 motor primitives, and 200+ everyday objects. 🦖 A tactile-reactive MoT architecture with spatial-temporal tactile encoding and asynchronous high-frequency tactile refinement. 🦖 A scalable training recipe combining 22,889 hours of human egocentric pretraining with tactile-grounded robot mid-training. Across 12 real-world contact-rich manipulation tasks, T-Rex achieves over 30% higher average success rate than the strongest baseline. We are fully open-sourcing the dataset, models, teleoperation stack, training code, and inference pipeline. 🌐 Project: tactile-rex.github.io/ 📄 Paper: arxiv.org/abs/2606.17055 💻 Code: github.com/ZhuoyangLiu2005/T… 🤗 Dataset: huggingface.co/datasets/zeka… 🧵 Thread ↓

Today, we enable AutoResearch in the physical world for the first time! Introducing ENPIRE: we give 8 Codex agents a fleet of robots, an allocation of GPUs, and generous token budget. We set them free with a simple goal: solve the task as quickly as possible, keep the robots busy but stay safe, don't waste precious compute. Make no mistake. Then humans step aside and our watch begins. The robot fleet starts to come alive: they learn to look for visual clues, reset the scene, practice novel skills, tinker with control stack, read papers online, debate, reflect, get stuck, and try again directly on the hardware. All we did is to give Codex an API to the world of atoms, and the rest is emergence. ENPIRE is able to solve high-precision tasks like tying zip-ties, organizing fine pins, and installing GPUs all by itself. We also discovered a new type of "physical scaling": 8 robots exploring in parallel improves significantly faster than fewer ones. A part of our NVIDIA GEAR lab now self-improves tirelessly over night. We just read the reports in the morning. /goal: we all take a holiday and Jensen wouldn't even notice ;) We will be open-sourcing everything, so you can host your self-running robot lab at home too! Deep dive in the thread:

This is actually sick! 🤯 A motion generator for robotics. and gaming. This is MotionBricks cooked by @NVIDIAAI. It's a 15,000 FPS real-time motion generation for robots and games. MotionBricks shipped to SIGGRAPH 2026 with code that integrates directly into NVIDIA's GR00T Whole-Body Control stack. 15,000 FPS, 2ms latency, 350,000 motion skills from a single neural backbone. Okay, let's have a look how it works: first train one generative model on 350k production-grade mocap clips (BONES-SEED dataset from Bones Studio). Then add "smart primitives" on top, a unified interface where you specify navigation targets, object interaction keyframes, and style prompts. The network generates everything else in real-time. There's no animation graph., and no per-task fine-tuning. Their demo-character navigates, picks up a sword, vaults a bench, sits down, switches between zombie/injured/skipping styles. Every frame generated by the network, in real-time. I think that this matters as MotionBricks is now core to GR00T Whole-Body Control which is the same stack powering humanoids widely used in research across the globe. Btw. code ships with an interactive G1 demo, but a full robotics-integrated release coming in ~1 month. The motion stack for humanoid robots is getting bigger! 🔥 Check it out here: nvlabs.github.io/motionbrick… cc: @NVIDIARobotics ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com

Exciting news on GR00T: NVIDIA announces our first open humanoid robot platform, featuring Unitree H2 Plus and Sharpa hands, to accelerate academic research and facilitate cross-institutional collaboration. R&D in humanoid robotics needs broader participation. Open science is how we build the future faster, together.

I will be in Vienna in two weeks to give a keynote at #ICRA2026. I'll share our recent progress on building generalist humanoid robots and show some of the latest results. Check out my talk on June 3: 2026.ieee-icra.org/program/k…

Now you can use GR00T N1.7 and SONIC together to enable tasks that require TRUE whole-body coordination!! Including simultaneous precise hand and foot placement, like opening a trash can with the foot pedal and throwing an object inside! Try it yourself, it is so fun!

GR00T-VisualSim2Real is now open source! VIRAL and DoorMan are now available with training code, simulation assets, and the full recipe for bringing visual sim-to-real loco-manipulation skills to your own humanoids. Repo: github.com/NVlabs/GR00T-Visu…

What is missing to bring real-time motion research into AAA games and real-world robotics? We present MotionBricks, a step toward bridging this gap with two key components: - a single generative latent motion backbone covering 350,000+ motion skills, running at 15,000 FPS with 2 ms latency and substantially improved quality and reliability. - a unified smart primitive interface for locomotion, object / scene interaction, with fine-grained control over generated behaviors. Webpage: nvlabs.github.io/motionbrick… Code: github.com/NVlabs/GR00T-Whol… Paper: arxiv.org/abs/2604.24833 (ACM TOG / SIGGRAPH 2026)