- NVIDIA will soon release Open Isaac Gr00T humanoid models for download on Hugging Face.
- GR00T Dreams Blueprint generates data to train humanoid robot reasoning and behavior.
- NVIDIA RTX Pro 6000 Blackwell workstations and RTX PRO servers help accelerate robot simulation and training by providing robust computing power for faster model development, data processing, and overall improved productivity in robot engineering tasks.
- Agility Robotics, Boston Dynamics, Foxconn, Lightwheel, Neura Robotics, and XPENG Robotics are among many robot makers adopting NVIDIA Issac.
At Computex, Nvidia announced the release of Nvidia ISAAC GR00T N1.5, the first update to its open and customizable foundation model for humanoid reasoning and skills, enabling users to create more adaptable robots tailored to specific applications and unique needs. The company also introduced NVIDIA ISAC GR00TD Dreams, a blueprint for generating synthetic motion data that helps accelerate robot learning and adaptability, as well as new NVIDIA Blackwell systems designed to reduce time-to-market for Vah humanoid robot development.
Companies like Agility Robotics, Boston Dynamics, Fourier, Foxlink, Galbot, Mentee Robotics, Neura Robotics, General Robotics, Skild AI, and XPENG Robotics are now adopting NVIDIA platform technologies. These technologies are helping to move humanoid robot development and deployment forward.
“Physical AI and robotics will bring the next industrial revolution,” said Jensen Huang, founder and CEO of Nvidia. From AI brains for robots to simulated worlds to practice in, or AI supercomputers for training core models, WE Media provides building blocks for every style of the robotics development journey.
New Isac GR00T Data Generation Blueprint closes the Data Gap
Presented during Huang’s Computex keynote, NVIDIA Isaac GR00T Dreams is a blueprint that generates large amounts of synthetic motion data, or neural trajectories, allowing physical AI developers to efficiently teach robots new behaviors and better adapt to changing environments, reducing the need for costly real-world data collection.
Developers can begin by post-training Cosmos-predicted world-based models (WFMs) for their robot using just a single image. gr00t dreams weekly creates videos showing the robot performing new tasks in different settings. The blueprint then extracts user-friendly action tokens from these videos, enabling developers to efficiently train robots to perform new tasks without extensive manual annotation.
The GR00T blueprint complements the Isaac GR00T Mimic blueprint, which was released at the NVIDIA GTC conference in March. While GR00T Mimic uses the NVIDIA Omniverse and the NVIDIA Cosmos platforms to augment existing data, GR00T Dreams uses Cosmos to generate entirely new data.
New Isac GR00T Models: Advanced Humanoid Robot Development
NVIDIA Research used the GR00T Dreams Blueprint to create synthetic training data, develop GR00T N1.5, and update GR00T N1 in only 36 hours. This process would have taken nearly three months if done manually.
GR00T N 1.5 is better at acclimating to new environments and workspace setups. It can also recognize objects based on user instructions. This capability greatly improves the model’s success rate on common material-handling and manufacturing tasks such as sorting or putting away objects. Early users of GR00T and models include AeiRobot, FoxLink, Lightwheel, and Neura Robotics. AEI Robot leverages these models to help Alice understand natural language instructions and perform complex pick-and-place tasks in factory environments. Foxlink Group utilizes them to enhance the flexibility and efficiency of industrial robot manipulators. Lightwheel applies the models to review synthetic data to accelerate the deployment of humanoid robots in factories. Neural Robotics is evaluating the models to advance its household automation work.
New Robot Simulation and Data Generation Frameworks Accelerate Training Pipelines
Developing advanced humanoid robots requires substantial and varied data, which can be costly to collect and process. Testing robots in real-world settings also entails additional costs and risks.
To help address the difficulties of data collection and testing, NVIDIA introduced these simulation technologies:
- NVIDIA Cosmos Reason, a new WFM that uses chain-of-thought reasoning to help curate higher-quality, more accurate synthetic data for physics. Physical AI model training is now available on Hugging Face.
- Cosmos Predict 2, used in GR00T Dreams, is coming soon to Hugging Face, featuring performance enhancements for high-quality world generation and reduced hallucination.
- NVIDIA Isaac GR00T: A blueprint for generating exponentially large quantities of synthetic motion trajectories for robot manipulation using just a few human examples.
- Open-source physical AI dataset now includes 24,000 high-quality human-humanoid robot motion trajectories, enabling faster, more accurate development and evaluation of GR00TN models and providing developers with a valuable free resource to accelerate project timelines.
- NVIDIA ISAC Sim 5.0: A simulation and synthetic data generation framework will soon be openly available on GitHub.
- NVIDIA ISIC Lab 2.2, an open source robot learning framework that will support new evaluation environments to help developers test GR00T N models.
Foxconn and Foxlink are using the GR00T Mimic Blueprint to accelerate their robotics training pipelines by generating synthetic motion manipulation. Agility Robotics, Boston Dynamics, Fourier, Mentee Robotics, Neura Robotics, and XPENG Robotics are simulating and training their humanoid robots using NVIDIA Isaac Sim and Isaac Lab. Skilled AI is using the simulation frameworks to develop general robot intelligence, and General Robotics is integrating them into its robot intelligence platform.
Universal Blackwell Systems For Robot Developers
Global systems manufacturers are building NVIDIA RTX Pro 6000 workstations and servers, supplying a single architecture that easily runs every robot development workload, from training and synthetic data generation to robot learning and simulation.
Cisco, Dari Technologies, Hewlett Packard Enterprise, Lenovo, and Supermicro announced NVIDIA RTX Pro–powered servers and Dari Technologies HBI. Lenovo announced NVIDIA RTX Pro 6000 Blackwell–powered workstations.
When developers need more computing power for large-scale training or data generation, they can use Nvidia Blackwell systems such as these. These are available on Nvidia DGX, in the cloud with top cloud providers, and through Nvidia Cloud Partners, and can deliver up to 18 times better data processing performance.
Developers will soon be able to deploy their robot-based models to the NVIDIA Jetson Thor platform. This will allow for faster One Robot inference and better runtime performance.
You can watch Huang’s Computex keynote and find out more at NVIDIA GTC Taipei.
Source: NVIDIA Powers Humanoid Robot Industry With Cloud-to-Robot Computing Platforms for Physical AI
