Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

This completely changes how robots can learn in simulation. NVIDIA just launched SimReady Central with 3,500+ simulation ready OpenUSD assets free and ungated on Hugging Face. ➝ Robot arms grippers hospital equipment warehouse gear kitchens. Lightwheel is the largest contributor to the library. Its assets are physics validated with... show more

Techniahqrobot | humanoid robots

19,154 subscribers

21,933 views • 12 days ago •via X (Twitter)

Science & Technology Education

Anya Rossi• Live Now

Private livecam show

7 Comments

Techniahqrobot | humanoid robots12 days ago

Here's what you need to know about SimReady Central. ‣ NVIDIA's open library launched at GTC Berlin and is hosted on Hugging Face, with 3,500+ assets in the live index and growing. ‣ Lightwheel is the largest contributor. ‣ These assets include validated mass and inertia collision geometry joint limits and contact behavior. ‣ The library covers hospitals and medical equipment, warehouses and logistics kitchens tools and workshops offices, automotive PPE and more. ‣ Lightwheel sits on the Technical Steering Committee of Newton the open source robotics physics engine under the Linux Foundation alongside NVIDIA Google DeepMind Disney Research and Toyota Research Institute. ‣ Lightwheel is a Physical AI infrastructure company working across data simulation evaluation and deployment for robotics.

Techniahqrobot | humanoid robots12 days ago

Explore the library and learn more. ➤ Library ➤ NVIDIA documentation ➤ Lightwheel's launch posts. Feel free to quote or reshare. ➝ LinkedIn ➝ X ➤ Lightwheel ➤ Lightwheel on Hugging Face

Techniahqrobot | humanoid robots12 days ago

Lightwheel ships open datasets, benchmarks and launches with NVIDIA and is building a small group of creators to work with regularly. The company is looking for creators with multi platform reach for its larger launches. If this first post goes well, Lightwheel wants to discuss a format covering all nine channels.

The SI Maximalist11 days ago

Robot school just got a prop department.

Techniahqrobot | humanoid robots11 days ago

We have everything we need to stop making excuses.

Steven Cheng12 days ago

validated inertia is teh real cheat code

Techniahqrobot | humanoid robots12 days ago

Accurate inertia can make a real difference when training manipulation policies

Related Videos

Introducing SimReadyGen. Agentic simulation generation for Physical AI. From a text prompt to physically accurate SimReady assets at scale. Robot training demands physically accurate environments at a scale traditional asset creation can't match. SimReadyGen closes that gap. Every generated asset carries measured physics, not estimates. Behind it is SimReady Foundry, our measured-physics pipeline: we measure how real objects behave — contact, friction, dynamics — and turn those measurements into ground-truth simulation parameters. Built on OpenUSD and integrated with NVIDIA Robotics Omniverse Libraries, SimReadyGen moves directly into Isaac Sim and Isaac Lab workflows. Try it today: Read the announcement: #PhysicalAI #Robotics #Simulation #OpenUSD

Introducing SimReadyGen. Agentic simulation generation for Physical AI. From a text prompt to physically accurate SimReady assets at scale. Robot training demands physically accurate environments at a scale traditional asset creation can't match. SimReadyGen closes that gap. Every generated asset carries measured physics, not estimates. Behind it is SimReady Foundry, our measured-physics pipeline: we measure how real objects behave — contact, friction, dynamics — and turn those measurements into ground-truth simulation parameters. Built on OpenUSD and integrated with NVIDIA Robotics Omniverse Libraries, SimReadyGen moves directly into Isaac Sim and Isaac Lab workflows. Try it today: Read the announcement: #PhysicalAI #Robotics #Simulation #OpenUSD

Lightwheel

12,358 views • 3 months ago

We just launched OpenUSD support on the Hugging Face Hub 🎉 Yesterday, NVIDIA Omniverse put 2,500+ SimReady assets on the Hub. Forklifts, robot arms, engines, hospital equipment, all OpenUSD. You can also search the whole library in plain English

We just launched OpenUSD support on the Hugging Face Hub 🎉 Yesterday, NVIDIA Omniverse put 2,500+ SimReady assets on the Hub. Forklifts, robot arms, engines, hospital equipment, all OpenUSD. You can also search the whole library in plain English

Caleb

398,808 views • 16 days ago

The coolest part of Microduck might not be the robot. It's how it learns to move. This repo shows the full reinforcement learning pipeline behind Microduck. The robot learns in simulation using MuJoCo, PPO and GPU-based training, then the trained policy is exported to ONNX and deployed on the real robot. But the real challenge is sim-to-real. The simulation models things like actuator behavior, battery voltage, command delays, friction and even gear backlash. That helps make the learned policy work when it moves from simulation to the physical robot. The repo includes policies for walking, fall recovery, sitting, standing, kicking, ground picking and even roller skating. Train in simulation. Test the behavior. Export the policy. Run it on a real robot.

The coolest part of Microduck might not be the robot. It's how it learns to move. This repo shows the full reinforcement learning pipeline behind Microduck. The robot learns in simulation using MuJoCo, PPO and GPU-based training, then the trained policy is exported to ONNX and deployed on the real robot. But the real challenge is sim-to-real. The simulation models things like actuator behavior, battery voltage, command delays, friction and even gear backlash. That helps make the learned policy work when it moves from simulation to the physical robot. The repo includes policies for walking, fall recovery, sitting, standing, kicking, ground picking and even roller skating. Train in simulation. Test the behavior. Export the policy. Run it on a real robot.

Praveen Kumar Verma

138,017 views • 2 months ago

NVIDIA just put 2,575 simulation-ready 3D assets on Hugging Face 🤯 A 472GB collection covering everyday objects, tools and industrial equipment. It’s called NVIDIA SimReady Assets. The models include physics settings and semantic labels for use in simulated environments. - Train robots to pick up and move objects - Build virtual factories and warehouses - Generate labelled training images - Populate 3D scenes with ready-made objects Useful for robotics research, digital twins and anyone building detailed virtual environments. Download the collection below 👇

NVIDIA just put 2,575 simulation-ready 3D assets on Hugging Face 🤯 A 472GB collection covering everyday objects, tools and industrial equipment. It’s called NVIDIA SimReady Assets. The models include physics settings and semantic labels for use in simulated environments. - Train robots to pick up and move objects - Build virtual factories and warehouses - Generate labelled training images - Populate 3D scenes with ready-made objects Useful for robotics research, digital twins and anyone building detailed virtual environments. Download the collection below 👇

Oliver

709,414 views • 13 days ago

Big news! World Labs' Marble is officially compatible with NVIDIA Robotics' Isaac Sim. Instead of showcasing their highlight reel, our team at Lightwheel created a full environment from a 360 pano using Marble and showed you can train directly in sim. The only non-generated assets are the robot arms, coffee, and bowl. Everything else was generated with Marble. Not sure how to do this yourself? NVIDIA wrote a technical blog with full instructions. You will learn how to: 1. Export scenes from World Labs' Marble as Gaussian splats 2. Convert to USD using NVIDIA Omniverse NuRec 3. Import into NVIDIA Isaac Sim 4. Add a robot and run the simulation Read the guide ➡️ #WorldModels #Robotics

Big news! World Labs' Marble is officially compatible with NVIDIA Robotics' Isaac Sim. Instead of showcasing their highlight reel, our team at Lightwheel created a full environment from a 360 pano using Marble and showed you can train directly in sim. The only non-generated assets are the robot arms, coffee, and bowl. Everything else was generated with Marble. Not sure how to do this yourself? NVIDIA wrote a technical blog with full instructions. You will learn how to: 1. Export scenes from World Labs' Marble as Gaussian splats 2. Convert to USD using NVIDIA Omniverse NuRec 3. Import into NVIDIA Isaac Sim 4. Add a robot and run the simulation Read the guide ➡️ #WorldModels #Robotics

Jonathan Stephens

13,783 views • 10 months ago

15–30 minutes of real-world robot data. That's now enough to go from sim-to-real failure to working robot. Let’s see… You train a robot in simulation. You deploy it in the real world. It fails. The physics don't match. So you try to fine-tune it with real data, but… you never have enough real data, and the fine-tuning breaks everything the simulation taught it. SimDist fixes this with one key decision: don't transfer the policy. Transfer the world model. Keep the reward and value knowledge from simulation frozen. Only update the part that's actually wrong, how the robot predicts physics. Now the robot doesn't have to relearn the entire task in the real world. It already knows what success looks like. It just needs to correct its understanding of how the real world moves. The part that makes this work: they also trained on failures and recoveries; not just perfect demonstrations. Without that, the planner finds the gaps and exploits them. With it, the robot can tell a good future from a bad one. That's all it needs. Results on peg insertion, table leg assembly, locomotion on slippery and uneven surfaces. Tasks that require precision, force, and quick reaction. Thanks for sharing, Tyler Westenbroek ([Tyler Westenbroek]. Interactive visualization + paper: ——- Weekly robotics and AI insights. Subscribe free:

15–30 minutes of real-world robot data. That's now enough to go from sim-to-real failure to working robot. Let’s see… You train a robot in simulation. You deploy it in the real world. It fails. The physics don't match. So you try to fine-tune it with real data, but… you never have enough real data, and the fine-tuning breaks everything the simulation taught it. SimDist fixes this with one key decision: don't transfer the policy. Transfer the world model. Keep the reward and value knowledge from simulation frozen. Only update the part that's actually wrong, how the robot predicts physics. Now the robot doesn't have to relearn the entire task in the real world. It already knows what success looks like. It just needs to correct its understanding of how the real world moves. The part that makes this work: they also trained on failures and recoveries; not just perfect demonstrations. Without that, the planner finds the gaps and exploits them. With it, the robot can tell a good future from a bad one. That's all it needs. Results on peg insertion, table leg assembly, locomotion on slippery and uneven surfaces. Tasks that require precision, force, and quick reaction. Thanks for sharing, Tyler Westenbroek ([Tyler Westenbroek]. Interactive visualization + paper: ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

14,222 views • 5 months ago

Physics simulation gives robots a place to learn before they enter the physical world. 🤖 Developers can model complex interactions, train and evaluate policies, and validate robot behavior in controlled virtual environments before deployment. Explore physics simulation for robotics 🔗

Physics simulation gives robots a place to learn before they enter the physical world. 🤖 Developers can model complex interactions, train and evaluate policies, and validate robot behavior in controlled virtual environments before deployment. Explore physics simulation for robotics 🔗

NVIDIA Robotics

30,701 views • 1 month ago

The RL era of robotics is here, and simulation is how to scale it. An early look at our research on a achieving a better scaling paradigm for robot learning with simulation 👇

The RL era of robotics is here, and simulation is how to scale it. An early look at our research on a achieving a better scaling paradigm for robot learning with simulation 👇

Moonlake

459,430 views • 2 days ago

Real → Sim → Robot — fast. With Gaussian splats, GRID brings physical assets into simulation tools like AirGen and NVIDIA Isaac Sim, enabling AI skills to be tested in sim and deployed to real robots in minutes. #Robotics #PhysicalAI #Simulation

Real → Sim → Robot — fast. With Gaussian splats, GRID brings physical assets into simulation tools like AirGen and NVIDIA Isaac Sim, enabling AI skills to be tested in sim and deployed to real robots in minutes. #Robotics #PhysicalAI #Simulation

General Robotics

12,257 views • 1 year ago

Robot skills start with the worlds they learn in. 🌍 Watch frontier AI agents use NVIDIA Isaac tools to build simulation worlds and train and evaluate robot skills. Then see how Agile ONE SN helps developers bring new skills into the real world.

Robot skills start with the worlds they learn in. 🌍 Watch frontier AI agents use NVIDIA Isaac tools to build simulation worlds and train and evaluate robot skills. Then see how Agile ONE SN helps developers bring new skills into the real world.

NVIDIA Robotics

16,520 views • 9 days ago

📢 Announcing one of the most exciting works from us this year on **scalable robot policy evaluation through real-to-sim transfer**, moving toward a scalable evaluation engine with structured world models that capture the appearance, geometry, and dynamics of environments involving deformable objects. 🤖 Evaluation remains one of the biggest bottlenecks in building general-purpose robots. Today, robots are still evaluated only in the real world, which is **orders of magnitude slower** than the development of language agents. We propose a new framework where simulation performance **strongly correlates** with the real world (r > 0.9), even for deformable objects. The key difference from existing work lies in the correlation between simulation and reality: if a robot model performs better in the digital world, does it also perform better in the real world? This question has long made people hesitant about simulation-based evaluation — especially for deformable objects. We are changing that. Our pipeline achieves effective real-to-sim transfer, establishing **state-of-the-art correlation** between simulation and reality for deformable object manipulation. It provides a **scalable and reproducible evaluation engine** for robot learning. 🌐

📢 Announcing one of the most exciting works from us this year on scalable robot policy evaluation through real-to-sim transfer, moving toward a scalable evaluation engine with structured world models that capture the appearance, geometry, and dynamics of environments involving deformable objects. 🤖 Evaluation remains one of the biggest bottlenecks in building general-purpose robots. Today, robots are still evaluated only in the real world, which is orders of magnitude slower than the development of language agents. We propose a new framework where simulation performance strongly correlates with the real world (r > 0.9), even for deformable objects. The key difference from existing work lies in the correlation between simulation and reality: if a robot model performs better in the digital world, does it also perform better in the real world? This question has long made people hesitant about simulation-based evaluation — especially for deformable objects. We are changing that. Our pipeline achieves effective real-to-sim transfer, establishing state-of-the-art correlation between simulation and reality for deformable object manipulation. It provides a scalable and reproducible evaluation engine for robot learning. 🌐

Yunzhu Li

39,960 views • 1 year ago

The next frontier of AI is physical. 🤖 From simulation to synthetic data, developers are training robots to understand the real world with NVIDIA Cosmos, Isaac simulation frameworks and open-source physics engine, Newton. Watch the video to see how partners like Hon Hai Technology Group (Foxconn), Hexagon, Humanoid, Noble Machines, and Skild AI are building the future. 🎥

The next frontier of AI is physical. 🤖 From simulation to synthetic data, developers are training robots to understand the real world with NVIDIA Cosmos, Isaac simulation frameworks and open-source physics engine, Newton. Watch the video to see how partners like Hon Hai Technology Group (Foxconn), Hexagon, Humanoid, Noble Machines, and Skild AI are building the future. 🎥

NVIDIA Robotics

21,664 views • 7 months ago

ROSClaw Integration Update Read the text in the video carefully ⊙ Robot is present both physically can in Nvidia Isaac Sim simulation. ⊙ The agent’s initial goal set to explore any object it finds in its spatial view. ⊙ Think of it as if the physical robot is seeing through the simulation. ⊙ ROSClaw vision uses Gemini Live for spatial narration ⊙ An object (a playground slide) is identified and the ROSClaw (OpenClaw) agent takes over. ⊙ The agent initiates action. Attempts to walk towards object. ⊙ None of it is pre-planned. The agent (robot) is acting on its own

ROSClaw Integration Update Read the text in the video carefully ⊙ Robot is present both physically can in Nvidia Isaac Sim simulation. ⊙ The agent’s initial goal set to explore any object it finds in its spatial view. ⊙ Think of it as if the physical robot is seeing through the simulation. ⊙ ROSClaw vision uses Gemini Live for spatial narration ⊙ An object (a playground slide) is identified and the ROSClaw (OpenClaw) agent takes over. ⊙ The agent initiates action. Attempts to walk towards object. ⊙ None of it is pre-planned. The agent (robot) is acting on its own

Irvin (in Japan 🇯🇵)

23,374 views • 8 months ago

Simulating and training robots requires a physics-first approach. 🤖 With Newton, the open-source physics simulation engine co-developed by Disney Research, Walt Disney Imagineering, Google DeepMind and NVIDIA, Disney’s BDX Droid can move through shifting sand and gravel with exceptional precision. Watch to see how Newton is playing a key role in teaching robots how to move, behave, and interact with different objects and materials in the real world. #CES2026

Simulating and training robots requires a physics-first approach. 🤖 With Newton, the open-source physics simulation engine co-developed by Disney Research, Walt Disney Imagineering, Google DeepMind and NVIDIA, Disney’s BDX Droid can move through shifting sand and gravel with exceptional precision. Watch to see how Newton is playing a key role in teaching robots how to move, behave, and interact with different objects and materials in the real world. #CES2026

NVIDIA Robotics

45,717 views • 9 months ago

Roboticists from Robotic Systems Lab and NVIDIA Embedded are teaching four-legged robots climb and jump. After training in simulation, the robots can autonomously decide how to scramble over and under obstacles, which will help them do dangerous jobs so that humans don't have to.

Roboticists from Robotic Systems Lab and NVIDIA Embedded are teaching four-legged robots climb and jump. After training in simulation, the robots can autonomously decide how to scramble over and under obstacles, which will help them do dangerous jobs so that humans don't have to.

Evan Ackerman

2,318,625 views • 3 years ago

Are We In a Simulation? Well, it does work as if we are. So for all intents and purposes you should adopt this mentality! Life responds to you, primarily your thoughts, beliefs, energy and actions. Whether or not there's a giant computer doesn't matter; simulation can simply be an allegory for how this reality operates. As such, we can work with this simulation to create the reality that we desire, and improve our lives. Becoming conscious is the goal. Then, work to understand WHY we are in this "simulation", and what our aims should be...

Are We In a Simulation? Well, it does work as if we are. So for all intents and purposes you should adopt this mentality! Life responds to you, primarily your thoughts, beliefs, energy and actions. Whether or not there's a giant computer doesn't matter; simulation can simply be an allegory for how this reality operates. As such, we can work with this simulation to create the reality that we desire, and improve our lives. Becoming conscious is the goal. Then, work to understand WHY we are in this "simulation", and what our aims should be...

⚡️🌞 Sol Brah 🌞🐬

19,309 views • 1 year ago

MEET BLUE: NVIDIA’S REAL-LIFE WALL-E Blue is an AI robot that moves, reacts, and talks like something straight out of a Pixar film. Blue runs on 2 NVIDIA computers and uses a physics engine for ultra-realistic tactile feedback, rigid and soft body simulation, all in real time. This isn’t a concept, this is how NVIDIA plans to train robots for the real world. And yes, it casually stole the show. Source: entrepreneurialogy

MEET BLUE: NVIDIA’S REAL-LIFE WALL-E Blue is an AI robot that moves, reacts, and talks like something straight out of a Pixar film. Blue runs on 2 NVIDIA computers and uses a physics engine for ultra-realistic tactile feedback, rigid and soft body simulation, all in real time. This isn’t a concept, this is how NVIDIA plans to train robots for the real world. And yes, it casually stole the show. Source: entrepreneurialogy

Mario Nawfal

38,388 views • 1 year ago