英伟达世界模型再进化，一个模型驱动所有机器人！机器人的GPT时刻真正到来

Core Insights - The main issue driving the entry of embodied intelligence into general domains is "cross-embodiment transfer" [1] - Current world models used in robotics and smart vehicles lack strong generalization and transfer capabilities, primarily being trained on fixed hardware platforms [1] - A true understanding of physical and causal relationships is necessary for effective transfer and generalization across different bodies and environments [1] Group 1: DreamZero Overview - NVIDIA's GEAR lab has introduced DreamZero, a world action model (WAM) based on a pre-trained video diffusion backbone network, enabling zero-shot capabilities [2] - DreamZero consists of 14 billion parameters and allows robots to perform previously unseen tasks with simple text prompts [3] - The model's code has been made open-source on GitHub [4] Group 2: Model Capabilities - DreamZero learns physical dynamics by jointly predicting future world states and actions, using video as a dense representation of world evolution [8] - It achieves over 2× improvement in generalization for new tasks and environments compared to the state-of-the-art VLA [8] - The model operates at 7Hz for real-time closed-loop control, demonstrating significant efficiency in cross-embodiment transfer with just 10-20 minutes of human or robot video demonstrations [8] Group 3: Experimental Results - In tests, DreamZero achieved 62.2% average task progress in zero-shot settings, significantly outperforming the best pre-trained VLA baseline at 27.4% [18] - For completely unseen tasks, DreamZero reached 39.5% task progress, while VLA struggled due to overfitting on dominant training behaviors [21] - DreamZero also excelled in adapting to new robots and objects with minimal training data, showcasing its efficiency in embodied transfer [26] Group 4: Real-Time Inference and Interactive Prompting - The model supports real-time inference with 150ms per action block, allowing for smooth execution and rapid response [28] - Interactive prompting capabilities enable users to directly instruct robots to perform new tasks in various environments [27] - DreamZero represents a new wave of foundational models for robotics based on video world models, indicating significant advancements in the field [30]