Introduction to Physical AI & Robotics at NVIDIA

NVIDIA Developer

4,473 views • 24 days ago

Video Summary

The video introduces "physical AI," defined as the embodiment of AI in the physical world, distinguishing it from other AI forms by its focus on action generation. It outlines a three-step framework for building physical AI: train, simulate, and deploy, supported by NVIDIA's hardware and software platforms. A key challenge highlighted is the data gap for robotics, which can be addressed by augmenting real-world data with synthetically generated data using NVIDIA Omniverse and Cosmos. Simulation-first development is emphasized as crucial for safety and cost-effectiveness. The deployment phase involves using foundational robot models and accelerated libraries like ROS 2 packages, all running on NVIDIA Jetson platforms. The presentation concludes with resources for learning and development, including developer forums, courses, and a 50% discount on Jetson dev kits.

An interesting fact revealed is that NVIDIA proposes a "three computer framework" (DGX for training, OBX/RTX Pro for simulation, and Jetson for deployment) to tackle physical AI challenges.

Short Highlights

Physical AI is the embodiment of AI in the physical world, transforming pre-programmed robots into intelligent agents that generate actions from inputs.
Building physical AI involves a three-step framework: Train, Simulate, and Deploy, supported by NVIDIA's DGX, OBX/RTX Pro, and Jetson computers, respectively.
A significant challenge in physical AI is the data gap for robotics, which can be addressed by using synthetic data generated with NVIDIA Omniverse and Cosmos to augment real-world data.
Simulation-first development is crucial for safely and cost-effectively testing robots, with tools like NVIDIA ISX Sim enabling realistic virtual environments.
Deployment of physical AI utilizes foundational robot models (e.g., Group 1.5) and accelerated libraries (e.g., ROS 2 packages) running on NVIDIA Jetson platforms for real-time performance.

Key Details

What is Physical AI? [03:53]

Physical AI is defined as the embodiment of AI in the physical world, moving beyond pre-programmed tasks to intelligent actions.
Unlike other AI forms that might generate text or images, physical AI models take inputs and instructions to generate physical actions.
Robots, cars, and even entire factories can be considered physical AI systems, with an exponential growth in their use for "dull, dangerous, and dirty jobs."

Physical AI is the manifestation or embodiment of AI in our physical world.

The Three Computer Framework for Physical AI [06:42]

Developing physical AI requires a three-stage process: training, simulation, and deployment.
NVIDIA offers specialized computers for each stage: DGX for training, OBX and RTX Pro for simulation, and Jetson for deployment.
NVIDIA's "three computer framework" aims to provide a comprehensive solution for building and deploying physical AI.

To build physical AI, you need to train the model, simulate situations where it will work, and then deploy the model.

Addressing the Robotics Data Gap [09:01]

Physical AI development faces a significant data challenge compared to other AI fields like large language models, which have vast internet data available.
Collecting real-world robotics data is costly and time-consuming, often limited by teleoperation.
A solution involves a combination of internet data, synthetic data, and real-world data, with synthetic data playing a crucial role in augmenting available information.

Physical AI is very hard to develop.

NVIDIA Omniverse and Cosmos for Synthetic Data [13:53]

NVIDIA Omniverse is used to generate realistic environments for simulation, while Cosmos acts as a world foundation model platform.
Together, Omniverse and Cosmos can generate synthetic data to help solve the robotics data gap.
World foundation models are key for data augmentation, enabling robots to reason about and predict within new environments.

Omniverse is our realistic simulation environment.

Simulation-First Development for Robotics [16:27]

Testing robots in the real world is extremely dangerous and expensive, leading to potential damage and delays.
Simulation-first development posits that every robot should be built and tested in simulation before physical deployment.
NVIDIA ISX Sim is an open-source, realistic virtual environment for software-in-the-loop and hardware-in-the-loop testing.

Every robot from now and forward is born in simulation.

ISX Sim and ISAC Lab Capabilities [18:41]

ISX Sim offers sim-ready assets, supports various robot models, and provides high-fidelity physics simulation.
ISAC Lab is a software built on ISX Sim for robot learning and policy training, utilizing reinforcement and imitation learning at scale.
Both ISX Sim and ISAC Lab are open-source and accessible via NVIDIA's Brev platform for GPU access.

ISAC lab is basically a gym for your robot.

Deploying Physical AI: Foundation Models and Libraries [24:43]

The deployment phase involves using foundational robot models, such as Group 1.5, which are generalized, customizable, and available on Hugging Face.
These models often have a dual-system architecture (System 1 and System 2) inspired by human cognition, enabling them to process vision, language, and action.
NVIDIA also provides over 30 accelerated ROS 2 packages for manipulation and mobility, enhancing performance on NVIDIA GPUs.

For deployment, we have the robot foundation models which you can customize for your robot and deploy it on a Jetson.

NVIDIA Jetson: The Platform for Deployment [31:40]

NVIDIA Jetson is the deployment platform for physical AI, offering high-performance computing required for real-time operations.
It supports sensor fusion and enables robots to rationalize data, reason, and act without significant latency.
Key Jetson platforms include Jetson AGX Thor for humanoid robots and Jetson AGX Orin, Orin Nano, and Orin Nano Super for various applications, with Orin Nano Super being a good starting point.

The ultimate platform for physical AI right now is Jetson AGX Thor.

Getting Started with Physical AI Resources [37:18]

For those looking to start, the robotics learning path, DLI courses, and hands-on projects with the open-source community are recommended.
Learning by doing, contributing to open source, and building a portfolio of projects are crucial for career transitions.
Robotics is multidisciplinary, so developing expertise in a few areas while understanding others is beneficial for collaboration.

To develop physical AI, there are three key things that you need to master.

Addressing Simulation-to-Reality Gap and Platform Suitability [45:49]

Acknowledging the sim-to-real gap, NVIDIA uses tools like Newton (an open-source physics engine) and ISAC Lab to bridge this challenge, especially for contact-rich manipulation.
NVIDIA's hardware platforms are specialized: DGX for training, RTX Pro for simulation, and Jetson for deployment.
Jetson community projects are recommended for finding real-time project ideas.