README.md

metadata

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.

Paper | Code | Models

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:

pip install -U huggingface_hub

Download only the evaluation data you need, for example LIBERO Scene2:

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:

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:

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:

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.

Citation

@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}
}