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pretty_name: SAGE Manipulation Datasets
task_categories:
- robotics
tags:
- robotics
- world-model
- planning
- libero
- robotwin
---
# SAGE Datasets
Manipulation datasets for **[SAGE: Subgoal-Conditioned Action Generation for Latent World Model Planning](https://arxiv.org/abs/2607.17973)**.
[Paper](https://arxiv.org/abs/2607.17973) | [Code](https://github.com/PKU-ML/SAGE) | [Models](https://huggingface.co/CLTRAY/SAGE)
## Data
| Environment | File prefix | Train | Validation | Evaluation |
|:---|:---|---:|---:|---:|
| LIBERO Scene2 | `libero_scene2` | 78.92 GB | 9.88 GB | 19.34 GB |
| LIBERO Caddy | `libero_caddy` | 68.43 GB | 8.56 GB | 8.64 GB |
| RoboTwin A2B | `robotwin_a2b` | 70.58 GB | 8.95 GB | 1.36 GB |
Sizes are archive sizes in decimal GB. Training and validation archives are under `training/`; evaluation archives are under `evaluation/`. Evaluation archives contain the inputs required by the released query manifests, not a complete training dataset. Simulator assets and model weights are installed separately.
## Download
Install the download client:
```bash
pip install -U huggingface_hub
```
Download only the evaluation data you need, for example LIBERO Scene2:
```python
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="CLTRAY/SAGE-data",
repo_type="dataset",
allow_patterns=["evaluation/libero_scene2_evaluation.tar"],
local_dir="sage-data",
)
```
To download Scene2 training and validation instead, use `allow_patterns=["training/libero_scene2_*"]`. Replace `libero_scene2` with `libero_caddy` or `robotwin_a2b` for the other environments. Omitting `allow_patterns` downloads all data (approximately 275 GB).
## Extract
Evaluation and validation archives are ordinary tar files:
```bash
mkdir -p datasets/libero_scene2
tar -xf sage-data/evaluation/libero_scene2_evaluation.tar -C datasets/libero_scene2
```
Training archives are split into numbered binary parts. **Merge them before extraction; individual parts are not separate tar archives.** After downloading all parts and the corresponding `.parts.json`, run:
```python
import hashlib
import json
from pathlib import Path
root = Path("sage-data")
manifest = json.loads((root / "training/libero_scene2_train.tar.parts.json").read_text())
archive = root / "training" / manifest["archive"]
full_hash = hashlib.sha256()
with archive.open("wb") as output:
for part in manifest["parts"]:
source = root / part["path"]
assert source.stat().st_size == part["bytes"], source
part_hash = hashlib.sha256()
with source.open("rb") as stream:
for block in iter(lambda: stream.read(8 * 1024 * 1024), b""):
part_hash.update(block)
full_hash.update(block)
output.write(block)
assert part_hash.hexdigest() == part["sha256"], source
assert archive.stat().st_size == manifest["bytes"]
assert full_hash.hexdigest() == manifest["sha256"]
print(f"Verified: {archive}")
```
Then extract the merged archive:
```bash
mkdir -p datasets/libero_scene2_train
tar -xf sage-data/training/libero_scene2_train.tar -C datasets/libero_scene2_train
tar -xf sage-data/training/libero_scene2_validation.tar -C datasets/libero_scene2_train
```
Allow disk space for the downloaded parts, merged archive, and extracted files. For environment setup, query manifests, and training/evaluation commands, see the [native benchmark guide](https://github.com/PKU-ML/SAGE/blob/main/NATIVE.md).
## Citation
```bibtex
@article{cheng2026sage,
title={SAGE: Subgoal-Conditioned Action Generation for Latent World Model Planning},
author={Cheng, Letian and Zhang, Qi and Wang, Yisen},
journal={arXiv preprint arXiv:2607.17973},
year={2026}
}
```
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