A range of developers, including RLWRLD, Skild AI, and Flexion, are already using NVIDIA’s latest technology. | Source: NVIDIA
NVIDIA Corp. today introduced GR00T 2, its updated open frontier world action model, and Isaac Lab 3.0, the latest version of its open-source robot learning framework. The company made these announcements at the Conference on Robot Learning, or CoRL, in Austin, Texas.
NVIDIA also gave a look at how its partners, like Ambi Robotics, Bosch Research, Dexmate, Dyna Robotics, FieldAI, Flexion, Noitom Robotics, Siemens, Skild AI, RLWRLD, UC Berkeley, Walden Robotics, and the University of Washington, are using these technologies.
In addition, NVIDIA hosted the MSIT-NVIDIA AI Startup Partnership Forum at COEX. There, the company announced an investment in South Korean startup Point2, with additional investments planned in Motif Technologies, RLWRLD, and Upstage.
This week is a precarious time for NVIDIA. Yesterday, the company‘s board opened an investigation into co-founder and CEO Jensen Huang. This was in response to Huang acknowledging to the NVIDIA board that he had an intimate relationship with a business executive that U.S. intelligence determined to be a Chinese spy.
In a statement, the board said it has “no reason to believe that either national security or NVIDIA secrets were compromised.”
NVIDIA steps up its world action model with GR00T 2
NVIDIA said it trained GR00T 2 on diverse robot and human data. The model includes 100,000 hours of NVIDIA open-source egocentric data and is intended to enables robot to learn dexterous manipulation skills.
Key features include:
- Out-of-the-box performance: GR00T 2 completed 40 of 50 evaluated tasks at least once without task-specific demonstrations.
- Skill transfer: It learns from human demonstrations and transfers skills between different robot bodies without robot demonstrations
- Generalization with little data: The model achieved 71% average success across 50 tasks with just one demonstration per task, increasing to 95% with five.
- Agent control: AI agents can use GR00T 2’s application programming interface (API) and supporting tools to run skills in sequence and recover from errors.
GR00T 2 currently ranks among the top three models across all vision-language-action (VLA) and world action model categories for the RoboCasa365, RoboLab, RoboDojo, and MolmoSpaces benchmarks, with multiple leaderboard wins, claimed NVIDIA.
The company asserted that its open workflows and models equip researchers with better tools for data collection and annotation. For researchers building robot-training datasets, NVIDIA’s new open Video to Data workflow turns human video into simulated robot demonstrations, with a challenge inviting researchers to advance it.
The NVIDIA EgoHand open model reconstructs 3D hand motion, trained with data from Noitom Robotics.
Nexuni is testing GR00T 2 for robotic laundry. Foxconn is applying GR00T 2 to factory automation for server manufacturing, and Siemens is evaluating it across industrial use cases.
MicroAGI is using GR00T 2, post-trained through reinforcement learning, to perform Ethernet cable swapping with a YAM robotic arm. Its agentic robotics approach automates the creation and calibration of simulation environments using Isaac Lab 3.0 and Newton, where the policy is trained.
Isaac Lab 3.0 provides the foundation for simulation training
Its latest release, Isaac Lab 3.0, allows researchers to use agents to quickly set up training environments with simulation-ready assets, switch between NVIDIA PhysX and Newton physics solvers, choose renderers for perception-based training, and apply a new reinforcement post-training library to foundation models such as GR00T.
Accurate physics is critical to transferring learned skills from simulation to the real robot, noted NVIDIA. Tighter integration with Newton, an open-source physics engine, now allows Isaac Lab to model deformable objects such as cables and cloth, along with complex articulated systems such as robotic hands with many interacting joints.
Together, Isaac Lab and Newton combine accurate physics with scale to enable researchers to train thousands of simulated robots in parallel. In a vision-based dexterous manipulation benchmark, Isaac Lab 3.0 with Newton reportedly achieved up to 2.5x the training throughput of Isaac Lab 2.3 with PhysX.
NVIDIA introduces new industrial benchmarks for robotics
Learning a skill is one challenge. Performing it reliably across changing conditions is another. NVIDIA’s Isaac Lab-Arena is an open-source framework for scaled policy evaluations. Isaac Lab-Arena enables developers to compose diverse and repeatable evaluations, then run policies across thousands of variations in parallel.
Analyses expose which conditions cause failures, so researchers know what to fix in their next training runs. NVIDIA released the developer preview of Version 0.4, at CoRL. DexBench and VABAR, two new open benchmarks, are among the first designed to measure robot dexterity and adaptability across industrial tasks using Isaac Lab-Arena and Newton.
DexBench, developed by RLWRLD with NVIDIA, takes dexterity evaluation off the tabletop and into real-world workflows, said the collaborators. Built using NVIDIA Isaac Lab-Arena, it features 15 tasks across four scenes spanning retail, logistics, manufacturing, and hospitality, testing generalization across object, spatial and physics variations.
Genesis AI, mimic robotics, and other industry partners are evaluating DexBench through early access. Teams can run DexBench on Manifold, Bifrost’s evaluation platform for scaling robotics simulations, with no setup required, built-in failure analysis and free credits for initial evaluations.
VABAR, the Variational Automation Benchmark for Agentic Robotics, from UC Berkeley and NVIDIA, evaluates robots across six industrial tasks. Each carries standardized variations, difficulty tiers, and success metrics.
How are robotics companies using NVIDIA technology?
Robotics developers are already putting NVIDIA’s latest technology to the test. A number of developers are using Isaac Lab 3.0 and Newton. At CoRL, Skild AI announced Scalable Throughput-optimized Engine for Embodied Learning, or STEEL, a new rigid-body physics solver built on Newton to accelerate robot training with Isaac Lab.
Flexion showcased how Isaac Lab and Newton help train Reflect, its robot-intelligence model combining reasoning and whole-body manipulation. FieldAI is using Newton, as well as Isaac Lab and Isaac Sim, to train and validate robot policies for deployment in unstructured real-world environments.
Dyna Robotics said it will demonstrate how parallel simulation in Isaac Lab helps train whole-body controllers for its semi-humanoid Taku robot for work in warehouses, hotels and food services.
Walden Robotics is using Isaac Sim’s photorealistic rendering to train and evaluate its general-purpose robot in virtual environments for manufacturing and logistics. Dexmate is exploring how coding agents can rehearse and refine manipulation strategies in Isaac Sim before executing them on its Vega robot.
AlphaNIST, built in Isaac Lab, demonstrates how efficient practice can translate into precision. A single policy learns all 20 tasks on the NIST Assembly Task Board, manipulating millimeter-scale parts using only a yes-or-no success signal. It achieves more than 98% training success, compared with 81% for separate per-task policies, using roughly 39x less GPU time.
Behind those results is Success-Guided Sampling, or SGS, from the University of Washington and NVIDIA. The method focuses practice on tasks a policy succeeds at about half the time, directing training toward skills it’s still learning. With SGS, robot training can scale beyond a million parallel environments.
The University of Texas at Austin’s School of Computing and Texas Robotics uses Isaac Sim and Isaac Lab to train robotic skills in simulation, letting students start developing before physical hardware is available.
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