World foundation models (WFMs) bridge the gap between the digital and physical realms. These powerful neural networks can simulate real-world environments and predict accurate outcomes based on text, image, or video input. This capability is critical for the development of physical AI systems like robots and autonomous vehicles (AVs), which require visually, spatially, and physically accurate data to learn effectively and safely.
WFMs are essentially large generative AI models that understand the dynamics of the real world, encompassing physics and spatial properties.
They learn to represent and predict real-world dynamics like motion, force, and spatial relationships from sensory data including text, images, videos, and movement. WFMs can then be used to create realistic synthetic videos of environments and interactions, which can be invaluable for training physical AI systems like robots and autonomous vehicles.
The Significance of World Foundation Models
The importance of WFMs lies in their ability to:
- Accelerate Training and Testing: WFMs provide virtual, 3D environments where developers can safely and efficiently train and test physical AI systems without the risks and costs associated with real-world trials. This is particularly beneficial for AVs and robots, which need to interact safely and effectively with complex and dynamic real-world scenarios.
- Generate Synthetic Data: Building world models traditionally requires vast amounts of real-world data, which can be difficult and expensive to collect. WFMs can generate synthetic data, providing a rich and diverse dataset that significantly enhances the training process. This synthetic data can address gaps in real-world data and enable training for scenarios that are difficult or dangerous to replicate in the real world.
- Improve Generalisation and Adaptability: WFMs contribute to better generalisation and adaptability of physical AI systems by integrating various input modalities, supporting transfer learning, and adjusting to environmental changes. This allows AI systems to perform effectively in a wider range of situations and adapt to new environments more easily.
Architectures for World Foundation Models
WFMs utilise two primary architectures:
- Diffusion models: These models begin with random noise and progressively refine it to generate high-quality video. They excel in tasks like video generation and style transfer.
- Autoregressive models: These models generate video one frame at a time, predicting the next frame based on the preceding ones. This makes them well-suited for predicting future frames or completing video sequences.
Developers can specialise these generalist models for downstream tasks using fine-tuning frameworks, leading to precise applications in areas like robotics and autonomous systems.
Applications of World Foundation Models
1. Autonomous Vehicles
WFMs offer significant benefits throughout the AV development pipeline:
- Training Data Generation: WFMs can generate pre-labeled, encoded video data that allows developers to easily curate and accurately train the AV stack. This data helps AVs understand the intent of surrounding vehicles, pedestrians, and objects.
- Scenario Generation: WFMs can create diverse and complex scenarios, including varying pedestrian behaviours, traffic patterns, and road conditions. This helps address gaps in training data and enables scaling testing to new locations and edge cases.
2. Robotics
WFMs help robots develop spatial intelligence by simulating virtual environments for them to learn and experiment in:
- Enhanced Data Efficiency: Simulated environments created by WFMs enhance data efficiency and allow for rapid iterations and simultaneous training processes. This accelerates the robot’s learning process and reduces the need for time-consuming and potentially risky real-world training.
- Safe Exploration: Training in simulated environments ensures safety by enabling robots to explore and learn in a controlled setting before being deployed in the real world.
- Complex Task Mastery: WFMs empower robots to master complex tasks by enabling advanced planning over extended horizons. This includes simulating interactions with objects, predicting human behaviors, and optimizing policy learning through simulated scenarios.
Benefits of World Foundation Models
WFMs offer a range of benefits for physical AI development:
- Realistic Video Generation: WFMs understand the underlying principles of how objects move and interact, enabling them to create more realistic and physically accurate visual content. This opens possibilities for generating realistic 3D worlds for various applications, including video games and interactive experiences. Additionally, highly accurate WFMs can generate synthetic data that can be used to train perception AI systems.
- Enhanced Generalisation and Decision Making: WFMs allow physical AI systems to learn and adapt to different environments by simulating actions and receiving feedback. Agents can “imagine” and plan future actions by simulating potential outcomes, leading to more informed decision-making.
- Improved Policy Learning: Policy learning involves finding the best strategies and actions for an AI system to take. WFMs provide a platform for evaluating different policies in simulated environments, leading to more efficient and effective policy learning.
- Optimising for Efficiency and Feasibility: WFMs incorporate cost models that help evaluate the efficiency and feasibility of different actions or strategies. By simulating various scenarios, these models can estimate the costs associated with decisions, aiding in optimising operations and making cost-effective choices in real-world applications.
Building World Foundation Models
Building WFMs requires a sophisticated process involving:
- Data Curation: This is a crucial step involving processing and preparing vast amounts of real-world data, primarily video and images. Data curation ensures high-quality data for training highly accurate models. It includes filtering, annotation, classification, and deduplication of image and video data.
- Tokenization: This process converts high-dimensional visual data into smaller units called tokens, which are easier for machine learning models to process. Tokenization compresses visual data into compact, semantic tokens, enabling efficient training of large-scale generative models.
- Fine-tuning: Foundation models are trained on extensive unlabeled datasets. Developers can fine-tune these pre-trained models for specific downstream tasks using additional data, adapting them for specific applications.
Cosmos World Foundation Models
NVIDIA Cosmos is a platform specifically designed to accelerate the development of physical AI systems using WFMs. It provides:
- Pre-trained WFMs: Cosmos offers pre-trained WFMs based on diffusion and autoregressive architectures, providing developers with a starting point for their projects.
- Tokenizers: Cosmos includes tokenizers that can efficiently compress videos into tokens for transformer models.
- Data Processing and Curation Pipeline: Cosmos features an accelerated data processing and curation pipeline, streamlining the process of preparing data for training WFMs.
NVIDIA is also actively collaborating with various companies to advance WFM development and address the challenges in physical AI development.