GoKawiilPre-training
Pre-training is about breadth: exposing the model to as much of the real world as possible. We use the embodiment-free UMI data described above, which spans a broad range of environments and tasks. At this scale, manual labeling is infeasible. Thus, we built an automatic annotation pipeline powered by a strong vision-language model. Long videos are split into fixed-length clips, and the VLM describes the state transition of grippers and interacting objects within each clip. The result is a large-scale corpus of real-world manipulation trajectories, each annotated with precise language descriptions. These allow the model to learn action generation that drives the scene toward the state transitions described by the language.
An encouraging finding is that pre-training shows a clean scaling behavior: as data and model size grow, validation action error steadily decreases.