Period
We built period, an end-to-end parking model with 11M parameters that runs at 120hz on a MacBook. It was trained from scratch from 7 hours of general driving data and 1 hour of task-specific trajectories and is able to park a car in an unseen parking lot! The core inspiration behind our system is Rhoda's DVA, which uses a causal video model pretrained on web-scale video to predict what the robot should see next, and a small inverse dynamics model to translate that into motor commands. We were also inspired by Standard Intelligence's FDM-1 which instead trains an IDM to label millions of hours of screen recordings with actions, then trains a forward model on next-action prediction from that data without requiring a world-model/IDM at inference time. For our final model architecture, we started from DIAMOND, which trains a diffusion UNet to predict future causal frames conditioned on an action input. We removed the action conditioning and added a small (17K parameter) action head coming