--- tags: - robotics - lerobot - video configs: - config_name: videos default: true drop_labels: true data_files: - split: train path: - "wipe_table/record_*/videos/**/*.mp4" - "checkout/record_*/videos/**/*.mp4" - "close_curtain/record_*/videos/**/*.mp4" - "cloth_basket/record_*/videos/**/*.mp4" - "fruit_basket/record_*/videos/**/*.mp4" - "make_bed/record_*/videos/**/*.mp4" - "pillow/record_*/videos/**/*.mp4" - "push_cart/record_*/videos/**/*.mp4" ---

WB-WAM

Selfcollected Dataset

English · [中文](README_zh.md) Real-robot data collected with SONIC for WB-WAM task post-training. LeRobot v3.0, 20 Hz, RGB 360 × 270. The release contains 1,011 episodes, 242,668 frames, 8 tasks, and 12 independent recordings (3.370 hours). Each `/record_XXXX/` is an independent LeRobot dataset. Recordings of the same task remain separate: ```text /record_XXXX/ data/chunk-000/file-000.parquet videos//chunk-000/file-000.mp4 meta/info.json meta/stats.json meta/tasks.parquet meta/episodes/chunk-000/file-000.parquet ``` The Dataset Viewer plays the MP4 files. The corresponding Parquet state/action rows and metadata are available under each recording in Files and versions. | Field | Meaning | | --- | --- | | `observation.state` | float32[110]: body state[9], G1 joint angles[29], G1 joint velocities[29], left/right Wuji hands[40], root[3] | | `action` | float32[136]: next SONIC token[64], left/right Wuji hands[40], G1 joints[29], root[3] | | `state_mask_110`, `action_mask_136` | bool[110/136]; `True` means invalid. Both hands have supervision. | | `timestamp`, `frame_index`, `episode_index`, `index`, `task_index` | Time and row/episode/task indices. | | `next.done` | Last frame of an episode. | The action contains **absolute next-frame joint targets** (+50 ms), not joint increments. Root[3] denotes roll, pitch, and body-frame z angular velocity, not XYZ position. The original train/validation labels are retained in episode metadata. See each record's `meta/info.json` for exact slices and camera keys. Download the unpacked files directly: ```bash hf download WB-WAM/Self-Collected --repo-type dataset --local-dir ./data/real_archive ``` To load one recording with LeRobot 0.4.4, use its local record directory as `root`: ```python from lerobot.datasets.lerobot_dataset import LeRobotDataset dataset = LeRobotDataset("WB-WAM/Self-Collected", root="./data/real_archive//record_XXXX") sample = dataset[0] ``` Training code and instructions: [WB-WAM Official](https://github.com/WB-WaM/WB-WAM-Official/tree/main/training).