Digital Event Horizon
NVIDIA has unveiled several groundbreaking advancements in simulation and agentic robotics, which are expected to transform the field of robotics. The company's GR00T 2 model has achieved impressive performance in dexterous manipulation tasks, and its integration with Isaac Lab has enabled researchers to train thousands of simulated robots in parallel. Agentic robotics is a new paradigm that integrates frontier agents to create interpretable code for physical robotics systems, and its use is expected to bring precision and scale to robot training. These advancements have the potential to transform the field of robotics, enabling robots to perform complex tasks with precision and autonomy, and are expected to have a significant impact on various industries.
NVIDIA has unveiled significant advancements in simulation and agentic robotics at the Conference on Robot Learning (CoRL), expected to transform the field of robotics. GR00T 2, an open frontier world action model, enables robots to learn dexterous manipulation skills with precision. NVIDIA Isaac Lab 3.0 provides a foundation for robot-learning framework, allowing researchers to quickly set up training environments with simulation-ready assets. The integration of Isaac Lab and Newton has enabled researchers to train thousands of simulated robots in parallel, accelerating the training process. Agentic robotics integrates frontier agents to create interpretable code for physical robotics systems, enabling complex task training and adaptability. NVIDIA EgoHand open model reconstructs 3D hand motion, trained with data from Noitom Robotics, expected to impact human-robot interaction and dexterous manipulation.
The world of robotics has witnessed a significant shift in recent times, with NVIDIA leading the charge in revolutionizing the way robots are trained and deployed in various industries. At the Conference on Robot Learning (CoRL) held in Austin, Texas, NVIDIA unveiled several groundbreaking advancements in simulation and agentic robotics, which are expected to transform the field of robotics.
One of the most significant developments in this space is the introduction of 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, GR00T 2 enables robots to learn dexterous manipulation skills with precision. This model has achieved impressive performance, completing 40 of 50 evaluated tasks at least once without task-specific demonstrations, and has shown remarkable skill transfer, learning from human demonstrations and transferring skills between different robot bodies without robot demonstrations.
Another significant advancement in this space is the introduction of NVIDIA Isaac Lab 3.0, which provides a foundation for robot-learning framework. Its latest release 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. This 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.
The integration of Isaac Lab and Newton has enabled researchers to train thousands of simulated robots in parallel, which has significantly accelerated the training process. This is a crucial step towards achieving the goal of robot autonomy, which 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.
The role of simulation in robot training cannot be overstated. Simulation has long been the proving ground for robots learning to walk, roll, and hop, and it is now being used to teach robots tasks that require a finer touch, such as aligning gears, connecting cables, and assembling small components. The shift from locomotion to manipulation is taking center stage at CoRL, alongside the rise of agentic robotics — a new paradigm that integrates frontier agents to create interpretable code for physical robotics systems.
Agentic robotics is a new paradigm that integrates frontier agents to create interpretable code for physical robotics systems. This approach helps 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.
The NVIDIA EgoHand open model, being demoed at CoRL, reconstructs 3D hand motion, trained with data from Noitom Robotics. This model is expected to have a significant impact on the field of robotics, particularly in areas such as human-robot interaction and dexterous manipulation.
The conference also saw the introduction of new open robotics technologies, including world action models, simulation tools, benchmarks, and agentic workflows. These advancements are expected to bring precision and scale to robot training, and are being used by leading researchers and companies in the field.
Several leading companies, including Ambi Robotics, Bosch Research, Dexmate, Dyna Robotics, FieldAI, Flexion, Noitom Robotics, Siemens, Skild AI, and UC Berkeley, are exploring these new technologies and are expected to make significant contributions to the field of robotics.
The impact of these advancements cannot be overstated. They have the potential to transform the field of robotics, enabling robots to perform complex tasks with precision and autonomy. They also have the potential to improve productivity, safety, and efficiency in various industries, including manufacturing, logistics, and healthcare.
In conclusion, the advancements in simulation and agentic robotics unveiled at CoRL are a significant step forward in the field of robotics. They have the potential to transform the way robots are trained and deployed, and are expected to have a significant impact on various industries. As researchers and companies continue to explore and develop these technologies, we can expect to see significant advancements in the field of robotics in the years to come.
Related Information:
https://www.digitaleventhorizon.com/articles/Revolutionizing-Robot-Training-NVIDIA-Unveils-Groundbreaking-Advances-in-Simulation-and-Agentic-Robotics-deh.shtml
https://blogs.nvidia.com/blog/robotics-simulation-advancements-corl/
Published: Mon Nov 9 12:12:20 2026 by llama3.2 3B Q4_K_M
