A 1X representative discusses how 1X World Model supports robot evaluation, data scaling, and decision-making at 1X.
Assessing a general-purpose robot model currently requires physical tests across many situations, potentially representing a lifetime of real-world experience. 1X presents its World Model as a way to connect simulated predictions with real robot behavior and make progress easier to measure.
The World Model predicts how the world will change after a robot acts, using raw sensory data from thousands of hours of robot interaction. It models examples including delicate-object grasping, rigid-body collisions, and objects deforming during handling.
1X says model quality improves as it adds data, especially examples where Neo encounters failures or unexpected outcomes. The model helps compare policies, including Redwood across tasks and environments, reducing evaluation cycles from weeks to minutes.
1X has explored web video, human first-person footage, and teleoperation, but identifies Neo's autonomous interaction with real environments as the most valuable data source. Neo attempts varied tasks and both succeeds and fails; its safety and compliance support sustained data collection in unstructured settings.
The World Model can also guide Neo's decisions by showing likely future states as it acts. Neo can use those predictions to plan, adjust its behavior, and respond to unexpected events.
Briefs summarise talks and interviews; timestamps open the video at that moment. Claims in a talk are the speaker's own.
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