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1X

1X World Model

平台
YouTube
时长
3:10
片中语言
英语
播放量
7.7万
镜头间隔中位数
4.42s
人脸出镜
41%
暗部画面
14%
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1X World Model 的文字记录

由语音模型根据影片自身的音轨转写，并非品牌撰写。点击任意一行即可跳到那一刻。

  1. 0:24Evaluating the quality of a model is a long-standing challenge in robotics.
  2. 0:28This makes it incredibly difficult to define a clear path towards improving autonomy.
  3. 0:33Currently evaluating a general purpose AI model requires running physical trials across the full spectrum of scenarios it might encounter.
  4. 0:40This corresponds to a lifetime of experience in the real world.
  5. 0:43That's why we built the 1x world model, which serves as a bridge between atoms and bits.
  6. 0:50The world model is a digital twin of the real world that predicts the future consequences of a robot's actions.
  7. 0:57this deep generative neural network allows us to compress that lifetime into an instant.
  8. 1:02The 1x world model tackles the complexity of the real world
  9. 1:05by learning directly from thousands of hours of robot interaction captured from raw sensory data.
  10. 1:10This enables us to model complex physical interactions,
  11. 1:14such as the grasping of delicate objects, collisions between rigid bodies,
  12. 1:18and the deformation of objects when manipulated,
  13. 1:20giving our model a firm understanding of the physics of the world around us.
  14. 1:24As we scale up our data, especially from interesting failure modes where Neo's task execution doesn't go exactly as planned,
  15. 1:31we find that the quality and accuracy of our world model reliably improves.
  16. 1:35This pattern of improvement mirrors trends observed in other large generative models, where the capability of the model scales with collecting the right type of data.
  17. 1:45Practically speaking, we leverage the world model to quickly assess and compare across many different policies.
  18. 1:50With a general model like Redwood that is trained to handle many different tasks across different environments,
  19. 1:56the world model lets us evaluate its capabilities with measurable results, shortening our iteration speed from weeks to minutes.
  20. 2:03Here at One X, we've experimented with different modalities and sources of data, including web-scale video data,
  21. 2:09first-person perspective human data, and teleoperation.
  22. 2:13But we found that the most important type of data for scaling up the capability of our world model
  23. 2:17is NIO autonomously interacting with real environments,
  24. 2:21attempting real tasks,
  25. 2:22and succeeding and failing in interesting and diverse ways.
  26. 2:26We are only able to collect this data at scale
  27. 2:29because NIO is a safe and compliant robot
  28. 2:31capable of continually running in unstructured environments.
  29. 2:35Beyond just evaluation,
  30. 2:37the world model can fundamentally change
  31. 2:39how NIO approaches decision-making.
  32. 2:40By continuously visualizing future states,
  33. 2:43NIO can effectively use the world model as its cognitive core,
  34. 2:46dynamically planning and adjusting its actions based on predicted outcomes.
  35. 2:50This predictive capability allows Neo to execute tasks more successfully, adapt more quickly,
  36. 2:56and handle unexpected events gracefully, bringing us closer to truly intelligent general-purpose robotics.
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