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## Environment Setup
<!-- Use a dedicated Conda environment for training: -->
```bash
conda create -n rise_training python=3.11.14 -y
conda activate rise_training
cd /path/to/RISE
bash install.sh
```
For deployment setup, please refer to the [deployment README](deploy.md).
## Data Preparation
The training code expects LeRobot-style data.
The data is first collect on Piper in hdf5 format and then converted to the lerobot format.
### Raw Data Collection
Please refer to the [data collection README](deploy.md) for details.
### Lerobot Conversion
Convert raw HDF5 logs collected on Piper into LeRobot-format data with:
```bash
cd /path/to/RISE/policy_and_value/policy_offline_and_value
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 \
```
Arguments:
- `--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.
The generated output follows the standard LeRobot layout shown below.
### Expected Lerobot Data Layout
```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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