I mentioned there was more exciting stuff to come after SGS 😀. @octi_zhang's latest work shows the kind of incredible emergent behaviour we can get from large scale RL in simulation. I've been applying these same ideas to other tasks, and have seen impressive results already!

The value of scaled RL training in simulation can't be understated. Huge congratulations to @octi_zhang and everyone else who worked on the SGS paper. What comes next is even cooler - stay tuned!

Fantastic work by @jayjunleee and the @NVIDIARobotics Dex research group. Always great to see new foundational sim2real work leveraging Isaac Lab!

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

Newton 1.5 is now available, making robot training at scale faster and more reliable. This release delivers: 🔁 More parallel simulation, lower memory, selective resets 🤝 Consistent contact physics 🤖 Experimental batched GPU control 📦 Cleaner USD/MJCF imports Get started: github.com/newton-physics/ne…

New in @ScienceMagazine 🎉 Really excited to share that SONIC is now published in Science Robotics! SONIC formulates super-scaling motion tracking as the foundational task for more natural and robust whole-body control. Our learned tracker and latent space further support foundation models like VLA/WAM training. Huge thanks to the team and collaborators who made this happen. science.org/doi/10.1126/scir… New checkpoints coming in today! #ScienceResearch #ScienceRoboticsResearch

Closing the sim-to-real gap for humanoid robotics starts with better simulation environments. Join us, @NianticSpatial, and @FlexionAI on Aug. 12 at 11 AM PT to see a real-to-sim workflow using 3D Gaussian splatting, Omniverse NuRec, Isaac, and OpenUSD. 📅 Add to calendar: nvda.ws/3SmCmSu

So cool! Just ordered mine! Any chance you guys will release the Isaac Lab training setup and USD models? Even better to be able to update the model on the robot…

1 million downloads and counting for Isaac Lab. 🥳 Thank you to the global robotics, embodied AI, and developer community using our open-source framework to train and develop the next generation of robots.

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 introduce TRON, a relighting framework for 3D captures. 💡TRON pairs a neural renderer with 3D Gaussian reconstructions, achieving realistic quality, with 3D, material, & lighting control at interactive frame rates. arxiv.org/abs/2606.11314 research.nvidia.com/labs/sil…

Super excited to open source Flashdreams, acceleration library for world models! No restrictions on usage, Apache 2 license. World models are the next frontier for video generation and paramount to Physical AI. Flashdreams yields out of the box speed up across world models

Faster-GS: github.com/nerficg-project/f… PPISP: research.nvidia.com/labs/sil… Btw, don't miss the PPISP oral if you're at CVPR next week. (3/3)

🚀We just released Asset Harvester, an image-to-3D model and end-to-end pipeline that extracts real object assets from autonomous driving videos! 🌐 Website: research.nvidia.com/labs/sil… 💻 Code: github.com/nvidia/asset-harv… [1/5] #AssetHarvester #AVSimulation #WorldModel #AutonomousDriving

Robots assembling robot brain -- imagine this kind of robustness on every precision manufacturing line! Live demo of GPU rack assembly at #NVIDIAGTC: - end-to-end neural network (Skild Brain) finetuned with little data - memory to perform long horizon task (placing jigs, 16 screwes, removing jigs) - robust to disturbances and fast to set up - no fancy sensors, just off-the-shelf arms and cameras

Excited to share the project that has surprised me the most in the last year! Large-scale RL in simulation, no demos and no reward engineering can solve dynamic, dexterous and contact rich tasks. The learned behaviors are reactive, forceful and use the environment for recovery in ways that are extremely challenging to bake in or teleoperate! You can play with the policies yourself to see: weirdlabuw.github.io/omnires… And, the learned behavior transfers to real world robots from RGB camera inputs! So what’s the trick - using simulator resets carefully! Let’s unpack (1/10)

We have seen rapid progress in humanoid control — specialist robots can reliably generate agile, acrobatic, but preset motions. Our singular focus this year: putting generalist humanoids to do real work. To progress toward this goal, we developed SONIC (nvlabs.github.io/GEAR-SONIC/), a Behavior Foundation Model for real-time, whole-body motion generation that supports teleoperation and VLA inference for loco-manipulation. Today, we’re open-sourcing SONIC on GitHub. We are excited to see what the community builds upon SONIC and to collectively push humanoid intelligence toward real-world deployment at scale. 🌐 Paper: arxiv.org/abs/2511.07820 📃 Code: github.com/NVlabs/GR00T-Whol…

This journey started for us attempting reconstruction of multicamera AV scenes with huge photometric differences like the one below. We wanted a principled way to handle ISP effects in #3DGS, which became PPISP. Big congrats to Isaac Deutsch, Nicolas Moenne-Loccoz and @ZGojcic !

I'm super excited for the future of Real2Sim2Real workflows on top of our new VoMP pipeline. There's still work ahead to integrate it into Isaac Lab through solvers in Newton and get simulation running fast enough for RL, but this is an important start. Congrats @rishit_dagli!