Simulation, which has long been the proving ground for robots learning to walk, roll and hop, is now helping researchers teach robots tasks that require a finer touch: aligning gears, connecting cables and assembling small components.
The shift from locomotion to manipulation is taking center stage at this week’s Conference on Robot Learning (CoRL) in Austin, Texas, alongside the rise of agentic robotics — a new paradigm that integrates frontier agents to create interpretable code for physical robotics systems.
At CoRL, NVIDIA is introducing new open robotics technologies, including world action models , simulation tools, benchmarks and agentic workflows.
Two NVIDIA papers are among six finalists out of thousands of submissions for the Best Paper Awards at CoRL:
GRAIL (Generating Humanoid Loco-Manipulation from World Models), which presents a digital generation pipeline for 3D assets and simulator-ready scenes for robot learning.
RoboTTT: Context Scaling for Robot Policies , which introduces a robot model and training recipe that scales visuomotor context three orders of magnitude beyond state-of-the-art policies without growing inference latency.
These advancements help researchers train robots on complex tasks, evaluate their performance across changing conditions and use failures to guide further learning — building the precision, adaptability and autonomy needed to move from lab demonstrations to industrial deployment.
NVIDIA is working with 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 to bring these advancements to the broader robotics community.
The next level of robot autonomy requires robots to perceive their surroundings, anticipate how their actions will change the environment and execute complex, multistep tasks with a high degree of precision. World action models enable exactly that.
At CoRL, NVIDIA is introducing GR00T 2 , an open frontier world action model built on NVIDIA Cosmos. Trained on diverse robot and human data, including 100,000 hours of NVIDIA open source human egocentric data , the model enables robots to learn dexterous manipulation skills. Key features include:
Out-of-the-box performance: Completed 40 of 50 evaluated tasks at least once without task-specific demonstrations.
Skill transfer: Learns from human demonstrations and transfers skills between different robot bodies without robot demonstrations
Generalization with little data: 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 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 and world action model categories for the RoboCasa365, RoboLab, RoboDojo and MolmoSpaces benchmarks, with multiple leaderboard wins.
Nexuni is testing it for robotic laundry handling, 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.
Training capable robots requires diverse data. Open NVIDIA 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 — being demoed at CoRL and with over 40,000 Hugging Face downloads in the two weeks since launch — reconstructs 3D hand motion, trained with data from Noitom Robotics .
A pretrained model gives robots a core set of skills. Simulation lets researchers train, refine and evaluate skills at scale before deploying them on physical robots.
NVIDIA Isaac Lab , an open source robot-learning framework, provides the foundation for that work. 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 like GR00T.
Accurate physics is critical to transferring learned skills from simulation to the real robot. 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.
Integration with Newton simulates how a strawberry deforms under a robot’s grip.
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 achieved up to 2.5x the training throughput of Isaac Lab 2.3 with PhysX.
Isaac Lab supports robot simulation with Newton and NVIDIA PhysX.
Key considerations
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Reference reading
- https://blogs.nvidia.com/blog/robotics-simulation-advancements-corl/#primary
- https://blogs.nvidia.com/blog/author/akhildocca/
- https://blogs.nvidia.com/blog/robotics-simulation-advancements-corl/#disqus_thread
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