| # Installation |
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| ## Environment Setup |
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| <!-- Use a dedicated Conda environment for training: --> |
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| ```bash |
| conda create -n rise_training python=3.11.14 -y |
| conda activate rise_training |
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| cd /path/to/RISE |
| bash install.sh |
| ``` |
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| For deployment setup, please refer to the [deployment README](deploy.md). |
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| ## Data Preparation |
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| The training code expects LeRobot-style data. |
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| The data is first collect on Piper in hdf5 format and then converted to the lerobot format. |
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| ### Raw Data Collection |
| Please refer to the [data collection README](deploy.md) for details. |
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| ### Lerobot Conversion |
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| Convert raw HDF5 logs collected on Piper into LeRobot-format data with: |
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| ```bash |
| cd /path/to/RISE/policy_and_value/policy_offline_and_value |
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| python examples/aloha_real/convert_to_lerobot.py \ |
| --data-dir /path/to/raw_dataset \ |
| --repo-ids aloha_mobile_dummy \ |
| --prompt "TASK_PROMPT" \ # e.g., "Pick up the block" |
| --save-dir /path/to/lerobot_output_root \ |
| --save_repoid output_dataset_name \ |
| ``` |
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| Arguments: |
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| - `--data-dir`: Root directory of one raw dataset collection. The script expects HDF5 files under `<data-dir>/<repo-id>/` and videos under `<data-dir>/<repo-id>/video/`. |
| - `--repo-ids`: One or more raw subdirectories to convert. For most RISE data, this is `aloha_mobile_dummy`. |
| - `--prompt`: Task description stored in the converted LeRobot dataset. |
| - `--save-dir`: Parent directory for converted output. |
| - `--save_repoid`: Name of the output dataset folder created under `--save-dir`. |
| - `--overwrite`: Optional. Remove an existing output folder and regenerate it. |
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| The generated output follows the standard LeRobot layout shown below. |
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| ### Expected Lerobot Data Layout |
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| ```text |
| <dataset_name>/ |
| ├── data/chunk-000/episode_*.parquet |
| ├── meta/ |
| │ ├── info.json |
| │ ├── episodes.jsonl |
| │ ├── episodes_stats.jsonl |
| │ └── tasks.jsonl |
| └── videos/chunk-000/*.mp4 |
| ``` |
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