Mirror notice: this is a repack of VLABench/vlabench_primitive_ft_lerobot_video into large tar archives for fast transfer (content unchanged; all credit to the original VLABench authors). Download and unpack via OpenWAM's scripts/download_assets/download_benchmark_data.py.

VLABench Primitive Tasks Dataset - LeRobot v3.0

Dataset Description

This dataset is organized in the LeRobot v3.0 format and is used for integrating VLABench into the LeRobot framework officially.

Compared with the v2.0 version and the RLDS version of the dataset, this release stores visual observations in a video-compressed format rather than as individual image files. This design provides significant advantages in both storage efficiency and data loading performance.

Task Coverage

This dataset includes 10 primitive tasks:

• add_condiment
• insert_flower
• select_book
• select_chemistry_tube
• select_drink
• select_fruit
• select_mahjong
• select_painting
• select_poker
• select_toy

Models trained on this dataset can be further evaluated on multiple generalization tracks in VLABench.

Baseline models are provided at:

• VLABench/pi05-primitive-10task
• VLABench/pi0-fast-primitive-10task
• VLABench/pi0-primitive-10task

Dataset Structure

The dataset structure follows the metadata specification in meta/info.json.

{
  "codebase_version": "v3.0",
  "robot_type": "panda",
  "total_episodes": 5000,
  "total_frames": 575101,
  "total_tasks": 128,
  "chunks_size": 1000,
  "data_files_size_in_mb": 100,
  "video_files_size_in_mb": 500,
  "fps": 10,
  "splits": {
    "train": "0:5000"
  },
  "data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
  "video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4",
  "features": {
    "image": {
      "dtype": "video",
      "shape": [480, 480, 3],
      "names": ["height", "width", "channel"],
      "fps": 10,
      "info": {
        "video.height": 480,
        "video.width": 480,
        "video.codec": "av1",
        "video.pix_fmt": "yuv420p",
        "video.is_depth_map": false,
        "video.fps": 10,
        "video.channels": 3,
        "has_audio": false
      }
    },
    "second_image": {
      "dtype": "video",
      "shape": [480, 480, 3],
      "names": ["height", "width", "channel"],
      "fps": 10,
      "info": {
        "video.height": 480,
        "video.width": 480,
        "video.codec": "av1",
        "video.pix_fmt": "yuv420p",
        "video.is_depth_map": false,
        "video.fps": 10,
        "video.channels": 3,
        "has_audio": false
      }
    },
    "wrist_image": {
      "dtype": "video",
      "shape": [480, 480, 3],
      "names": ["height", "width", "channel"],
      "fps": 10,
      "info": {
        "video.height": 480,
        "video.width": 480,
        "video.codec": "av1",
        "video.pix_fmt": "yuv420p",
        "video.is_depth_map": false,
        "video.fps": 10,
        "video.channels": 3,
        "has_audio": false
      }
    },
    "state": {
      "dtype": "float32",
      "shape": [7],
      "names": ["state"],
      "fps": 10
    },
    "actions": {
      "dtype": "float32",
      "shape": [7],
      "names": ["actions"],
      "fps": 10
    },
    "timestamp": {
      "dtype": "float32",
      "shape": [1],
      "names": null
    },
    "frame_index": {
      "dtype": "int64",
      "shape": [1],
      "names": null
    },
    "episode_index": {
      "dtype": "int64",
      "shape": [1],
      "names": null
    },
    "index": {
      "dtype": "int64",
      "shape": [1],
      "names": null
    },
    "task_index": {
      "dtype": "int64",
      "shape": [1],
      "names": null
    }
  }
}

Citation

If you use this dataset, please cite:

@article{zhang2024vlabench,
  title={Vlabench: A large-scale benchmark for language-conditioned robotics manipulation with long-horizon reasoning tasks},
  author={Zhang, Shiduo and Xu, Zhe and Liu, Peiju and Yu, Xiaopeng and Li, Yuan and Gao, Qinghui and Fei, Zhaoye and Yin, Zhangyue and Wu, Zuxuan and Jiang, Yu-Gang and others},
  journal={arXiv preprint arXiv:2412.18194},
  year={2024}
}