Large world models (LWMs) are advanced machine learning models for simulating real-world environments. Although still an emerging technology, many AI researchers believe they have the potential to significantly transform applications and industries like entertainment, video, gaming, movies, healthcare, virtual reality, and engineering. Moreover, LWMs are seen as particularly important for the next generation of sophisticated robots and advanced autonomous vehicles (AVs). This Advisor provides an overview of LWM technology and examines its potential for use in robotics and AV development.
Underlying Technology
LWMs are designed to model and understand the dynamics of the real world. The underlying principle of LWMs is to create a virtual, 3D representation of the (actual) world that AI developers can use to train and test physical AI systems. LWMs are trained on huge datasets, including text, photos, video, and audio. This allows the model to build accurate internal representations of the complex physical interactions and spatial properties of the world and how it works and provides the ability to reason about the consequences of actions.
Importance for Robotics & AVs
By learning to represent and predict dynamics like motion, force, and spatial relationships, LWMs can generate highly detailed and accurate simulations of real-world scenarios. This includes virtual environments for robots and AVs to learn to perform key tasks like manipulation, navigation, and obstacle avoidance.
By incorporating diverse data types such as text, images, video, and audio, multimodal AI models can learn to understand and generate more complex and nuanced information. For example, a multimodal AI model might be trained on text, images, and video to enable an AV’s navigation system to better interpret and understand the context of road and traffic conditions on a city street. Similarly, a robot model could use video, images, and text data to improve its machine vision capabilities, allowing it to perform a wider range of tasks in a factory. Adding audio data to the training mix can enhance speech recognition and natural language understanding and generation capabilities, supporting multiple operating scenarios.
This approach leverages the strengths of each data type, resulting in more accurate and versatile outputs. In essence, multimodal training makes the model more robust and capable of handling a broader range of tasks and scenarios. (As an aside, it is also considered important for developing models that may eventually achieve artificial general intelligence.)
Benefits for Robotics & AVs
Using LWMs for robotic and AV development brings several potential benefits:
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Improved navigation. The advanced simulation capabilities provided by LWMs offer a virtual environment for safely training and testing the guidance and navigation systems required for robots and AVs to operate efficiently (and safely) in complex environments, such as warehouses, factories, city streets, highways, and other scenarios.
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Enhanced perception. By creating highly accurate models of the environment they are intended to operate in, LWMs will allow robots and AVs to better understand their surroundings and the interactions they encounter when operating. Enhanced perception will benefit a variety of onboard systems, including path-planning, motion-control, steering and braking, and obstacle-detection programs.
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Improved safety and obstacle avoidance. Because LWMs can predict future outcomes and scenarios, robotic-control systems and AV accident-avoidance programs will be able to predict potential obstacles with greater accuracy and plan safer, alternate routes. This will help reduce the risk of a robot or AV (e.g., car, drone) colliding with surrounding obstacles, other vehicles, or people (e.g., warehouse or factory workers).
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Greater autonomy. With the ability to reason about the consequences of actions, LWMs can support real-time decision-making in dynamic environments, thereby facilitating a higher level of autonomy in robots and AVs. This is particularly important for robots and AVs operating in complex or dangerous environments where human supervision is difficult or impractical to provide. For example, a robotic explorer operating on Mars, upon encountering obstacles, would be able to evaluate and decide the best path to proceed (as opposed to having to first transmit its sensor readings to Earth-based mission control and then wait to receive instructions).
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Synthetic data generation. LWMs can generate large volumes of synthetic data for training AI systems, allowing robotic or AV systems developers to create large datasets more representative of the domain or environment they are modeling. This capability is especially important for model-building efforts with applications where there is insufficient data or data that is difficult to collect (e.g., training AI models for AVs intended to function in extreme weather conditions and for robotic space exploration).
Conclusion
The ability of LWMs to model and understand real-world dynamics makes them highly promising to AI researchers aiming to develop the next generation of AI systems, including advanced robots and AVs. By implementing LWMs, robotics and AVs can achieve higher levels of autonomy and safety through enhanced perception, improved navigation, and optimized obstacle avoidance, making them more reliable and efficient in real-world applications.
However, several challenges need to be addressed in developing LWMs. These include high costs related to computing infrastructure and energy consumption, data collection and privacy concerns, and the fact that the technology is still emerging and not well understood. These factors currently place LWM development beyond the reach of many organizations. As a result, various companies, ranging from start-ups to Big Tech players, are developing LWMs to offer to end-user organizations. These include Decart, Google/DeepMind, Nvidia, Odyssey, OpenAI, and World Labs.
In Part II of this Advisor series, we’ll examine what companies are doing to develop LWMs for robotics and AV development. In the meantime, I’d like to get your opinion about LWMs in general and, in particular, what you think they mean for the development of more sophisticated robots and advanced AVs. As always, your comments will be held in strict confidence. You can email me at experts@cutter.com or call +1 510 356 7299 with your comments.
