README.md

metadata

license: apache-2.0
task_categories:
  - robotics
  - imitation-learning
tags:
  - rlds
  - tfds
  - open-x-embodiment
  - vlabench
  - lerobot
  - franka
  - robot-learning
pretty_name: VLABench RLDS Delta EEF

VLABench RLDS Delta EEF

This dataset is a TFDS/RLDS conversion of VLABench/vlabench_composite_ft_lerobot_video for the VQ-VLA / OXE RLDS training pipeline.

The converted action space is delta end-effector pose:

[dx, dy, dz, droll, dpitch, dyaw, gripper_command]

Source Dataset

• Source: VLABench/vlabench_composite_ft_lerobot_video
• Original format: LeRobot v3.0 parquet + MP4 videos
• Robot: Franka
• Episodes: 5,977
• Frames / transitions: 2,539,771
• Tasks: 167
• FPS: 10
• Camera views: front, secondary, wrist
• Image resolution: 224 x 224 RGB

RLDS Layout

Use this dataset as a local TFDS directory by placing it under an OXE data root:

/path/to/OXE/vlabench/1.0.0

The converted steps feature contains:

• observation/image: JPEG bytes, primary/front RGB view, 224 x 224
• observation/second_image: JPEG bytes, secondary RGB view, 224 x 224
• observation/wrist_image: JPEG bytes, wrist RGB view, 224 x 224
• observation/EEF_state: float32[6], [x, y, z, roll, pitch, yaw]
• observation/gripper_state: float32[1]
• observation/joint_state: float32[7]
• observation/state: float32[8], [EEF_state, 0.0 padding, gripper_state]
• action: float32[7], delta EEF pose plus gripper command
• language_instruction: task instruction string
• reward, discount, is_first, is_last, is_terminal

Action Conversion

The source LeRobot actions field stores absolute EEF target poses:

[target_x, target_y, target_z, target_roll, target_pitch, target_yaw, gripper]

During conversion, each action is transformed into a delta pose relative to the current EEF observation:

delta_xyz = target_xyz - current_eef_xyz
delta_rpy = (R_target * R_current.inv()).as_euler("xyz")
gripper_command = clip(source_gripper, 0, 1)

roll, pitch, and yaw use XYZ Euler angles.

Conversion Command

The dataset was generated with:

python scripts/data/vlabench/convert_vlabench_lerobot_to_rlds.py \
  --input-root /nvme/cuiluyi/resources/datasets/VLABench/vlabench_composite_ft_lerobot_video \
  --output-root /nvme/cuiluyi/resources/datasets/OXE \
  --num-shards 64 \
  --num-workers 8 \
  --jpeg-quality 3 \
  --ffmpeg-threads 1 \
  --overwrite

The converter decodes the source AV1 MP4 videos with ffmpeg/libdav1d and stores per-frame JPEG bytes in TFRecord shards. It also scans the actual parquet files to correct source metadata entries whose recorded data file pointer does not match the local file layout.

OXE Configuration

The corresponding OXE config is:

"vlabench": {
    "image_obs_keys": {"primary": "image", "secondary": "second_image", "wrist": "wrist_image"},
    "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
    "state_obs_keys": ["EEF_state", None, "gripper_state"],
    "state_encoding": StateEncoding.POS_EULER,
    "action_encoding": ActionEncoding.EEF_POS,
}

Validation

The converted dataset was checked with:

• tfds.builder_from_directory(...) on vlabench/1.0.0
• Metadata checks: 5,977 trajectories, 2,539,771 transitions, 64 shards
• Source-to-converted sampled checks across multiple shards:
  • max_action_diff == 0.0
  • max_eef_diff == 0.0
  • max_grip_diff == 0.0
  • max_state_diff == 0.0
  • max_joint_diff == 0.0
• OXE make_single_dataset(...) smoke test with primary, secondary, and wrist images, proprio state, and 7D action loading successfully