Introduction
AI in robots is the driving force behind how robots learn to be robots, enabling them to adapt and evolve through sophisticated algorithms.
Everything that moves will be autonomous. Physical AI will embody robots of every kind in every industry.
Three computers built by NVIDIA enable a continuous loop of robot AI simulation, training, testing, and real-world experience.
Training robots requires huge volumes of data. Internet scale data provides common sense and reasoning.
But robots need action and control data, which is expensive to capture.
With blueprints built on Nvidia, Omniverse and Cosmos, developers can generate massive amounts of diverse synthetic data for training robot policies.
Enhancing Robot Learning:
Data Augmentation and Simulated Testing with NVIDIA Tools
First, in Omniverse, developers aggregate real world sensor or demonstration data according to their different domains, robots and tasks.
Then use Omniverse to condition cosmos. Multiplying the original captures into large volumes of photo real diverse data.
Developers use Isaac Lab to post train the robot policies with the augmented data set.
And let the AI robots learn new skills by cloning behaviors through imitation learning, or through trial and error with reinforcement learning AI feedback. Practicing in a lab is different than the real world. New policies need to be field tested.
Developers use omniverse for software and high-fidelity sensor simulation.
NVIDIA’s MEGA: Testing Collaborative Robot Fleets at Scale in Omniverse
Real world operations require multiple robots to work together.
MEGA, an omniverse blueprint, lets developers test fleets of post train policies at scale.
Here, Foxconn tests heterogeneous robots in a virtual Nvidia Blackwell production facility.
As the robot brains execute their missions, they perceive the results of their actions through sensor simulation, then plan their next action.
MEGA lets developers test many robot policies, enabling the robots to work as a system, whether for spatial reasoning, navigation, mobility, or dexterity.
Introducing NVIDIA Isaac Groot none: A Foundation Model for Humanoid Robots
Amazing things are born in simulation. Today, we’re introducing NVIDIA Isaac Groot none.
Groot none is a generalist foundation model for humanoid robots.
It’s built on the foundations of synthetic data generation and learning and simulation.
Groot none features a dual system architecture for thinking fast and slow, inspired by principles of human cognitive processing.
A Deep Dive into its Dual-System Architecture and Versatile Capabilities
The slow thinking system lets the robot perceive and reason about its environment and instructions and plan the right actions to take.
The fast-thinking system translates the plan into precise and continuous robot actions.
Groot n one’s generalization lets robots manipulate common objects with ease and execute multi step sequences collaboratively.
And with this entire pipeline of synthetic data generation and robot learning,
Humanoid robot developers can post train Groot none across multiple embodiments and tasks across many environments.
Around the world, in every industry, developers are using NVIDIA’s three computers to build the next generation of embodied AI.
conclusion
In summary, NVIDIA is constructing a holistic ecosystem to speed the arrival of autonomous robots in every sector.
Their strategy involves the creation of huge amounts of synthetic data with Omniverse and Cosmos, training and testing advanced robot policies with Isaac Lab and MEGA.
And the creation of powerful foundation models such as Isaac Groot none.
Through an emphasis on accurate simulation, multi-robotic collaboration, and adaptive AI that can sense and act in sophisticated environments,
NVIDIA is enabling global developers to build the next wave of embodied intelligence, making the dream of a fully autonomous future increasingly possible.