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license: mit
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---
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license: mit
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---
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# Dataset Card
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This is the fine-tuning dataset used in the paper *RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation*.
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## Source
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- Project Page: https://rdt-robotics.github.io/rdt-robotics/
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- Paper:
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- Code:
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- Model: https://huggingface.co/robotics-diffusion-transformer/rdt-1b
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## Getting Started
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Download all archive files and use the following command to extract:
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```bash
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cat rdt_data.tar.gz.* | tar -xzvf -
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```
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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:
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```
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rdt_data/---task_1/---episode_1.hdf5
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|-task_2/ |-episode_2.hdf5
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|-task_3/ |-...
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|-... |-expanded_instruction_gpt-4-turbo.json
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```
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Each HDF5 file corresponds to a trajectory/episode of the task, which has the following keys:
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- `observations`:
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- `qpos`: joint positions of the two robot arms, (TRAJ_LEN, 14); the seventh and fourteenth joints are gripper joint angles
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- `images`:
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- `cam_high`: RGB images from the exterior camera at each time step, (TRAJ_LEN, 480, 640, 3)
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- `cam_left_wrist`: RGB images from the left-wrist camera at each time step, (TRAJ_LEN, 480, 640, 3)
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- `cam_right_wrist`: RGB images from the right-wrist camera at each time step, (TRAJ_LEN, 480, 640, 3)
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- `action`: desired joint positions of the two robot arms at the next time step,
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(TRAJ_LEN, 14); Note that this is slightly different from the actual joint positions at the next time step
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Note: The number of HDF5 episode files is not necessarily consecutive. `TRAJ_LEN` may vary from episode to episode.
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Each JSON file corresponds to the annotated language instructions of the task, which has the following keys:
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- `instruction`: original human-annotated instructions; its value is a string
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- `expanded_instruction`: instructions generated by GPT-4-Turbo by expanding the original one; its value is a list of strings
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- `simplified_instruction`: instructions generated by GPT-4-Turbo by simplifying the original one; its value is a list of strings
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