物理AI解答“把大象放进冰箱需要几步？”

Core Insights - The article explores the capabilities of physical AI in bridging the gap between the information world and the physical world, using the metaphor of getting an elephant into a refrigerator to illustrate the complexities involved in robotic task execution [1][12]. Group 1: Virtual Environment Construction - The first step involves creating a virtual model of the "elephant-refrigerator" scenario, which serves as a testing ground for technology validation. NVIDIA's Omniverse allows for the construction of digital twin spaces that accurately replicate physical laws, ensuring reliable AI training and reasoning [2][3]. - Omniverse is not just a 3D modeling tool; it is a real-time collaboration and simulation platform based on OpenUSD standards, capable of millimeter-level replication of the physical world [2][3]. - The integration of NVIDIA Cosmos enables rapid generation of training environments by allowing engineers to input text or reference images, significantly reducing the time required for virtual scene construction [3][4]. Group 2: AI Understanding and Reasoning - The next step is to teach AI to comprehend the physical attributes of the elephant and the refrigerator, which requires a model capable of physical understanding and logical reasoning. NVIDIA's Cosmos Reason is designed to enable robots to think through task processes rather than merely executing preset commands [5][6]. - Cosmos Reason is a customizable visual language model (VLM) with 7 billion parameters, allowing robots to interpret complex commands and break them down into executable actions [6][7]. - The model can analyze the dimensions of the elephant and the refrigerator in real-time, generating a sequence of actions to accomplish the task while considering potential mechanical failures [7]. Group 3: Training and Deployment - NVIDIA proposes a "three-computer" concept to support the entire lifecycle of physical AI, which includes a DGX system for training, an AGX platform for deployment, and the Omniverse+Cosmos for simulation and data generation [8][9]. - The DGX system provides the necessary computational power to process vast amounts of virtual scene data for training, optimizing the task breakdown logic and enhancing the model's robustness through reinforcement learning [9]. - The AGX platform is designed for real-time deployment, allowing the trained model to operate in real-world scenarios by quickly processing sensor data and issuing action commands [10]. Group 4: Simulation and Data Generation - Omniverse serves as a crucial link in the "three-computer" framework, enabling the simulation of extreme scenarios to gather training data for physical AI, which is otherwise costly and time-consuming to obtain in reality [11][12]. - The ability to simulate thousands of extreme scenarios in Omniverse allows for the generation of extensive datasets necessary for training physical AI, thereby reducing the costs and risks associated with real-world data collection [12]. - The successful execution of the "elephant into the refrigerator" task signifies a pivotal step in the application of physical AI, with NVIDIA's technology poised to impact various industries, expanding the influence of computing from a $5 trillion information industry to a $100 trillion physical world market [12][13].