Synthetic training data for embodied AI
Kosmic Worlds trains long-horizon world models that generate synthetic video data for robotics and autonomous vehicle teams, covering the rare scenarios and edge cases that real-world data collection can't reach at scale.
Most video generation breaks down after a few seconds, losing track of objects, geometry, and motion. Kosmic Worlds is built around long-horizon coherence: keeping a scene physically consistent over minutes, not seconds, so the output is usable as real training data, not just a demo reel.
V2 is in development now, with a driving-specific finetune, V2D, planned for autonomous vehicle customers on top of the same base model.
Synthetic data that's structured for model training: action-conditioned rollout, world-state memory, and consistency across long sequences.
Building a robotics or autonomous vehicle team and dealing with training data constraints? Reach out directly, we're talking with ML and simulation leads as we build V2.