Add dataset card with model links and download instructions

README.md

@@ -0,0 +1,107 @@
+---
+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}
+}
+```