The development of physical AI systems, such as robots on factory floors and autonomous vehicles on streets, relies heavily on large, high-quality datasets for training. However, collecting real-world data is expensive, time-consuming, and is often limited to some major tech companies. NVIDIA’s COSMOS platform addresses this challenge by using advanced physics simulations to address the scales to generate realistic synthetic data. This enables engineers to train AI models without the costs and delays associated with collecting real-world data. This article discusses how Cosmos can improve access to basic training data and accelerate the development of secure, reliable AI for real-world applications.
Understand physical AI
Physical AI refers to an artificial intelligence system that can perceive, understand and act within the physical world. Unlike traditional AI, where text or images can be analyzed, physical AI must deal with complexities in the real world such as spatial relationships, physical forces, and dynamic environments. For example, self-driving cars need to identify pedestrians, predict their movements and adjust their paths in real time, taking into account factors such as weather and road conditions. Likewise, robots in warehouses must precisely drive obstacles and manipulate objects.
Developing physical AI is challenging because it requires a lot of data to train models of various real-world scenarios. Collecting this data, whether it’s driving video recording or robotic task demonstrations for hours, can be time-consuming and expensive. Additionally, testing AI in the real world can take risks, as errors can lead to accidents. NVIDIA COSMOS addresses these challenges by generating realistic synthetic data using physics-based simulations. This approach simplifies and accelerates the development of physical AI systems.
What is the basic world model?
At the heart of Nvidia Cosmos is a collection of AI models called the World Foundation Model (WFM). These AI models are specially designed to simulate virtual environments that are closely mimicked with the physical world. By generating videos or scenes of physical consciousness, WFM simulates how objects based on spatial relationships and laws of physics interact. For example, WFM can simulate a car driven through a heavy rain, showing how water affects traction or how headlights reflect wet surfaces.
WFM is crucial for physical AI because they provide a secure, controllable space for training and testing AI systems. Instead of collecting real-world data – realistic simulations of environments and interactions, developers can use WFM to generate comprehensive data. This approach not only reduces costs, but also speeds up the development process and allows testing of complex rare situations (such as unusual traffic conditions) without the risks associated with real-world testing. WFMS is a general model that can be fine-tuned for a specific application, similar to applying large language models to tasks such as translation or chatbots.
Revealing Nvidia Cosmos
NVIDIA COSMOS is a platform designed to enable developers to build and customize WFM for physical AI applications, especially autonomous vehicles (AVs) and robotics. Cosmos integrates advanced generative models, data processing tools and security features to develop AI systems that interact with the physical world. The platform is open source with a license-enabled model.
Key components of the platform include:
- Generate the World Foundation Model (WFM): Pre-trained models that simulate physical environments and interactions.
- Higher Symbolist: Tools to effectively compress and process data for faster model training.
- Accelerate data processing pipeline: Systems for large data sets powered by NVIDIA’s computing infrastructure.
The main novelty of the universe is its inference model of physical AI. This model provides developers with the ability to create and modify virtual worlds. They can tailor the simulation to specific needs, such as testing the robot’s ability to pick up objects or assess the AV’s response to sudden obstacles.
The main features of Nvidia Cosmos
Nvidia Cosmos provides various components that address specific challenges in physical AI development:
- Universe Transfer WFM: These models take structured video inputs, such as segmentation maps, depth maps, or LIDAR scans, and generate controllable, realistic video outputs. This feature is particularly useful for creating synthetic data to train perceived AI, such as systems that help AVS recognize objects or robots identify their surroundings.
- Cosmic Prediction WFM: The cosmic prediction model generates virtual world states based on multi-modal inputs, including text, images, and video. They can predict future scenarios, such as how the scenarios develop over time, and support multi-frame generation of complex sequences. Developers can customize these models using NVIDIA’s physical AI dataset to meet their specific needs, such as predicting pedestrian motion or robotic motion.
- Cosmic reasons wfm: The Cosmos Cause Model is a fully customizable WFM with space-time awareness. Its reasoning ability allows it to understand spatial relationships and how they change over time. The model uses thoughtful reasoning to analyze video data and predict results, such as if a person steps into a crosswalk or if a box falls off a shelf.
Applications and Use Cases
Nvidia Cosmos has already had a significant impact on the industry, with several leading companies adopting the platform for its physical AI projects. These early adopters emphasized the versatility and practical impact of the universe on various departments:
- 1 times: Use the universe for advanced robotics to improve its ability to develop AI-driven robots.
- Agile robotics: Expand partnership with NVIDIA to use the universe for humanoid robot systems.
- Figure AI: Utilize the universe to advance humanoid robot technology, focusing on AI that can perform complex tasks.
- Former Telix: Apply the universe in autonomous vehicle simulation to generate various test solutions.
- Skating AI: Use the universe to develop AI-driven solutions for various applications.
- Uber: Integrate the universe into its autonomous vehicle development to improve training data for autonomous driving systems.
- Oaks: Use the universe to accelerate industrial flow automation.
- Virtual incision: Explore the universe of surgical robotics to improve the accuracy of healthcare.
These use cases show how the universe can meet a wide range of needs from transportation to healthcare by providing synthetic data that trains these physical AI systems.
What the future means
The launch of NVIDIA COSMOS is very important for the development of physical AI systems. By providing an open source platform with powerful tools and models, NVIDIA is giving access to physical AI development to a wider range of developers and organizations. This could lead to significant advancements in multiple areas.
In autonomous transportation, enhanced training data and simulations may lead to safer and more reliable self-driving cars. In robotics, faster development of robots capable of performing complex tasks can transform industries such as manufacturing, logistics, and healthcare. In healthcare, technologies such as surgical robotics explored through virtual incisions can improve the accuracy and results of medical procedures.
Bottom line
The NVIDIA universe plays a crucial role in the development of physical AI. The platform allows developers to generate high-quality synthetic data by providing a physics-based world-based model (WFM) to create realistic simulations. With its open source access, advanced features and ethical safeguards, Cosmos can develop AI faster and more efficiently. The platform has already driven significant advances in industries such as transportation, robotics and healthcare by providing synthetic data for building intelligent systems that interact with the physical world.
