加利福尼亚州帕洛阿尔托, Jan. 13, 2026 (GLOBE NEWSWIRE) -- 1X 欣然宣布推出全新的 1X 世界模型 (1X World Model)。这一为 NEO 量身打造的突破性 AI 更新，标志着人形机器人领域迈出关键性一步。 借助全新的 1X 世界模型，NEO 能够基于符合现实物理规律的视频模型，按需将任何指令即时转化为可执行的 AI 能力。 这标志着迈出了通向未来的关键第一步——在那个未来，机器人将具备自我学习能力，完成所有人类能够胜任的任务。通过此次更新，NEO 得以融合经机器人数据精调的互联网规模视频数据，从而执行各类 AI 任务，甚至能够应对此前从未接触过的物体和环境。 这种方法打通了数字智能与物理现实之间的闭环，使 NEO 得以承载并延展以视频形式记录的人类浩瀚知识积累，并将其转化为现实世界中的行动能力。“在多年持续打磨世界模型、并将 NEO 的整体设计尽可能向人类靠拢之后，如今 NEO 已能够从互联网规模的视频数据中学习，并将所学直接应用于物理世界。 它甚至能在毫无先例的情况下，将任意指令转化为全新的操作，这标志着 NEO 开始具备自我学习能力，从而执行几乎任何你能想 ...

Core Insights - 1X Technologies has launched the world's first humanoid robot world model, 1X World Model, which demonstrates significant advancements in technology and application scenarios [1][2] - The model utilizes video generation technology and end-to-end autonomous driving world models to simulate how the real world evolves under the influence of intelligent agents [2][3] Group 1: Model Capabilities - The 1X World Model showcases controllable actions, allowing it to generate different outcomes based on various action commands, demonstrating diverse generation characteristics from the same initial frame [3][7] - It accurately simulates interactions between objects, enabling the robot to lift and move objects while keeping others stationary under specified conditions [5][10] - The model can predict the consequences of executing precise actions in various scenarios, such as opening doors and wiping surfaces, showcasing its ability to generate physically plausible future states [8][10] Group 2: Evaluation and Performance - The evaluation of the model's performance has been enhanced through the collection of over 3000 hours of real operational data, allowing it to learn from diverse tasks in home and office environments [16][18] - The model's ability to predict future states and task success rates has been validated against real-world performance, establishing a robust feedback mechanism for model optimization [18][20] - Empirical evidence shows that checkpoints with higher performance in the 1X World Model evaluation tend to perform better in real assessments, indicating a strong correlation between predicted success rates and actual task scores [20][21] Group 3: Data Scaling and Transfer Learning - The research indicates a positive correlation between data volume and prediction accuracy, confirming that increasing data size improves the model's performance across various tasks [25][32] - Experiments demonstrate that the model can effectively transfer knowledge from one task to another, enhancing its ability to generalize from accumulated experiences [35][40] - The model's performance is significantly improved when trained with specific task data, allowing it to adapt to unfamiliar tasks and environments more effectively [40][41] Group 4: Future Implications - The advancements in the 1X World Model suggest a potential "data singularity" in robotics, where AI-generated data becomes indistinguishable from real data, revolutionizing training methodologies [41][42] - The model's success could accelerate the commercialization of household service robots and reshape the competitive landscape of the AI industry [42]