1x-technologies/worldmodel

Dataset for the 1X World Model Challenge.

Download with:

huggingface-cli download 1x-technologies/worldmodel --repo-type dataset --local-dir data

Current version: v1.1

• magvit2.ckpt - weights for MAGVIT2 image tokenizer we used. We provide the encoder (tokenizer) and decoder (de-tokenizer) weights.

Contents of train/val_v1.1:

• video.bin - 16x16 image patches at 30hz, each patch is vector-quantized into 2^18 possible integer values. These can be decoded into 256x256 RGB images using the provided magvig2.ckpt weights.
• segment_ids.bin - for each frame segment_ids[i] uniquely points to the segment index that frame i came from. You may want to use this to separate non-contiguous frames from different videos (transitions).
• actions/ - a folder of action arrays stored in np.float32 format. For frame i, the corresponding action is given by joint_pos[i], driving_command[i], neck_desired[i], and so on. The shapes and definitions of the arrays are as follows (N is the number of frames):
  • joint_pos (N, 21): Joint positions. See Index-to-Joint Mapping below.
  • driving_command (N, 2): Linear and angular velocities.
  • neck_desired (N, 1): Desired neck pitch.
  • l_hand_closure (N, 1): Left hand closure state (0 = open, 1 = closed).
  • r_hand_closure (N, 1): Right hand closure state (0 = open, 1 = closed).

  Index-to-Joint Mapping

   {
        0: HIP_YAW
        1: HIP_ROLL
        2: HIP_PITCH
        3: KNEE_PITCH
        4: ANKLE_ROLL
        5: ANKLE_PITCH
        6: LEFT_SHOULDER_PITCH
        7: LEFT_SHOULDER_ROLL
        8: LEFT_SHOULDER_YAW
        9: LEFT_ELBOW_PITCH
        10: LEFT_ELBOW_YAW
        11: LEFT_WRIST_PITCH
        12: LEFT_WRIST_ROLL
        13: RIGHT_SHOULDER_PITCH
        14: RIGHT_SHOULDER_ROLL
        15: RIGHT_SHOULDER_YAW
        16: RIGHT_ELBOW_PITCH
        17: RIGHT_ELBOW_YAW
        18: RIGHT_WRIST_PITCH
        19: RIGHT_WRIST_ROLL
        20: NECK_PITCH
    }
  

We also provide a small val_v1.1 data split containing held-out examples not seen in the training set, in case you want to try evaluating your model on held-out frames.