Datasets:
Size:
10M<n<100M
License:
| license: apache-2.0 | |
| pretty_name: 1X World Model Challenge Dataset | |
| size_categories: | |
| - 10M<n<100M | |
| viewer: false | |
| Dataset for the [1X World Model Challenge](https://github.com/1x-technologies/1xgpt). | |
| Download with: | |
| ``` | |
| huggingface-cli download 1x-technologies/worldmodel --repo-type dataset --local-dir data | |
| ``` | |
| Current version: v2.0 | |
| Contents of train/val_v2.0 | |
| - **videos** - | |
| - **segment_indicies** - For video `i` and frame `j`, `segment_idx_i[j]` uniquely points to the segment index that frame `j` came from. You may want to use this to separate non-contiguous frames from different videos (transitions). | |
| - **robot_states** - States arrays defined in `Index-to-Joint Mapping` stored in `np.float32` format. For video `i` and frame `j`, the corresponding state is given by `states_i[j]`. | |
| - **metadata** - The `metadata.json` file provides high-level information about the entire dataset, while `metadata_[i].json` files contain specific details for each individual video `i`. | |
| #### Index-to-State Mapping (NEW) | |
| ``` | |
| { | |
| 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 | |
| 21: Left hand closure state (0 = open, 1 = closed) | |
| 22: Right hand closure state (0 = open, 1 = closed) | |
| 23: Linear Velocity | |
| 24: Angular Velocity | |
| } | |
| Previous version: v1.1 | |
| - **magvit2.ckpt** - weights for [MAGVIT2](https://github.com/TencentARC/Open-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 (OLD) | |
| ``` | |
| { | |
| 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. | |