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metadata
license: mit
tags:
  - robotics
  - multimodal
  - finetuning
  - vla
  - rdt

Dataset Card

This is the fine-tuning dataset used in the paper RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation.

Source

Uses

Download all archive files and use the following command to extract:

cat rdt_data.tar.gz.* | tar -xzvf -

In the resulting rdt_data folder, each sub-folder is a task. In each task sub-folder, we have HDF5 files and the instruction JSON file, as illustrated below:

rdt_data/---task_1/---episode_1.hdf5
          |         |
          |-task_2/ |-episode_2.hdf5
          |         |
          |-task_3/ |-...
          |         |
          |-...     |-expanded_instruction_gpt-4-turbo.json

Each HDF5 file corresponds to a trajectory/episode of the task, which has the following keys:

  • observations:
    • qpos: joint positions of the two robot arms, (TRAJ_LEN, 14); the seventh and fourteenth joints are gripper joint angles
    • images:
      • cam_high: RGB images from the exterior camera at each time step, (TRAJ_LEN, 480, 640, 3)
      • cam_left_wrist: RGB images from the left-wrist camera at each time step, (TRAJ_LEN, 480, 640, 3)
      • cam_right_wrist: RGB images from the right-wrist camera at each time step, (TRAJ_LEN, 480, 640, 3)
  • action: desired joint positions of the two robot arms at the next time step, (TRAJ_LEN, 14); Note that this is slightly different from the actual joint positions at the next time step

Note: The number of HDF5 episode files is not necessarily consecutive. TRAJ_LEN may vary from episode to episode.

Each JSON file corresponds to the annotated language instructions of the task, which has the following keys:

  • instruction: original human-annotated instructions; its value is a string
  • expanded_instruction: instructions generated by GPT-4-Turbo by expanding the original one; its value is a list of strings
  • simplified_instruction: instructions generated by GPT-4-Turbo by simplifying the original one; its value is a list of strings