Datasets:

nvidia
/

PhysicalAI-Robotics-Locomanipulation-GRAIL

license: apache-2.0
task_categories:
  - robotics
tags:
  - humanoid-locomanipulation
  - whole-body-control
  - human-object-interaction
  - video-to-motion
  - reinforcement-learning
  - physics-simulation
  - isaac-sim
  - unitree-g1
  - smpl-x
  - 4d-hoi-reconstruction
library_name: GRAIL

GRAIL: Generating Humanoid Loco-Manipulation from 3D Assets and Video Priors

Project Page | Paper | Code | Docs

Dataset Overview

Tabletop Pickup	Ground Pickup

Tabletop Manipulation	Ground Manipulation

Sitting	Curb

Slope	Stairs

Description:

This dataset contains physics-validated 4D human-object interaction (HOI) trajectories for the Unitree G1 humanoid robot. It is generated by GRAIL (Generating Humanoid Loco-Manipulation from 3D Assets and Video Priors), an end-to-end pipeline that (1) acquires a 3D asset, (2) generates a synthetic character-object interaction video in Blender + Kling AI, (3) reconstructs the 4D HOI (SMPL-X human pose + object 6-DoF) from the video, (4) retargets the human motion to the G1 skeleton, and (5) drives a SONIC tracking policy in Isaac Lab — the released motion data is what the simulated G1 + object realize in simulation.

The release is partitioned by HOI category. Each motion ships with: the source synthetic video, the 4D HOI reconstruction (SMPL-X + object pose), the retargeted G1 robot trajectory, the post-RL object trajectory, and the object's USD asset (textures preserved).

The repo also ships the submodule checkpoints required to re-run the full GRAIL pipeline end-to-end (GEM-SMPL human pose estimation + FoundationPose object 6-DoF tracking + SONIC task general tracking).

Sample Usage

You can run stages of the GRAIL pipeline end-to-end using the following commands (requires environment setup as described in the Code repository):

# 3D asset generation (procedural terrain or AI-generated objects)
python -m grail.pipelines.gen_terrain --type stairs --num 50 --output_dir data/syn_stairs
conda run -n hunyuan python -m grail.pipelines.gen_3d_assets \
    -i configs/gen_3d/example_objects.yaml -o data/gen_example

# 2D HOI generation (Blender + Kling video)
python -m grail.pipelines.gen_2dhoi --dataset ComAsset --category cordless_drill \
    --character kid --results_dir results --video_model_api kling-ai

# 4D HOI reconstruction
python -m grail.pipelines.recon_4dhoi --dataset ComAsset --category cordless_drill --results_dir results

License/Terms of Use:

Use of the released dataset is governed by the Apache License, Version 2.0. The bundled checkpoints under checkpoint/ retain their respective upstream licenses:

NVIDIA-produced (checkpoint/GEM-SMPL/outputs/, checkpoint/FoundationPose/, checkpoint/SONIC/) — NVIDIA Open Model License Agreement.
Third-party (checkpoint/GEM-SMPL/inputs/: HMR2, ViTPose, VIMO, YOLOv8x, SMPL-X body model) — governed by the licenses of their respective upstream releases; please consult each upstream project before use.

Object USD assets under data/<hoi_category>/object_usd/ come from four asset sources, each with different downstream-license implications:

RoboCasa-derived (pickup_table, pickup_ground) — derivative works of RoboCasa (UT Austin, NVIDIA), licensed under CC BY 4.0. Attribution to the RoboCasa Team is required when redistributing or building on these specific files.
ComAsset-derived (advanced manipulation) — sourced from ComAsset (Kim et al., ECCV 2024), licensed under ODC-By v1.0. Attribution to the ComAsset authors is required. Individual meshes in ComAsset are originally collected from SketchFab and carry their own per-mesh licenses (listed in categories.json of the upstream ComAsset repo) — consult those for any downstream commercial redistribution.
Hunyuan3D-2.1 generated (advanced manipulation, stairs) — outputs of Hunyuan3D-2.1. Per the Tencent Hunyuan 3D 2.1 Community License, model outputs are user-owned and not subject to model-license restrictions, so these are released here under Apache License 2.0. The Hunyuan3D-2.1 model itself (not used or redistributed by this dataset) carries territorial, MAU, and acceptable-use constraints — consult the upstream license if you intend to use the model.
Procedurally generated (curb, slope, stairs) — GRAIL-original outputs, released under Apache License 2.0.

The license: apache-2.0 declared in the dataset metadata applies to the GRAIL-original outputs: motion trajectories, 4D HOI reconstructions, and per-motion metadata under data/<hoi_category>/{robot,objects,recon,meta}/, plus procedurally generated and Hunyuan3D-generated object assets. Bundled checkpoints under checkpoint/, RoboCasa-derived assets, and ComAsset-derived assets retain their respective upstream licenses as described above.

Use Case:

GRAIL is intended for use by individuals and professionals in fields such as robotics learning, machine learning, computer vision, and physics-based animation. Specific use cases include:

4D HOI reconstruction — study 4D human-object interaction from a paired dataset of (synthetic video, SMPL-X recon, object 6-DoF, retargeted humanoid trajectory).
Humanoid policy training — supervise RL or imitation-learning trackers on physically validated reference motions to learn whole-body loco-manipulation skills on the Unitree G1.
Sim-to-real transfer — use the G1 trajectories directly as targets for a deployable controller, or as kinematic references for a learned residual policy.

Reference(s):

Project page: https://research.nvidia.com/labs/dair/grail/
Paper: https://arxiv.org/abs/2606.05160
Code: https://github.com/NVlabs/GRAIL
Documentation: https://NVlabs.github.io/GRAIL/

Dataset Layout:

nvidia/PhysicalAI-Robotics-Locomanipulation-GRAIL/
├── data/<hoi_category>/
│   ├── video/<hoi_category>__<object>__<NNN>.mp4       # source synthetic video
│   ├── recon/<hoi_category>__<object>__<NNN>.pkl       # 4D HOI recon (SMPL-X + object 6-DoF)
│   ├── robot/<hoi_category>__<object>__<NNN>.pkl       # post-RL G1 robot trajectory (29 body DOFs + hand_dof_pos)
│   ├── objects/<hoi_category>__<object>__<NNN>.pkl     # post-RL object 6-DoF trajectory
│   ├── meta/<hoi_category>__<object>__<NNN>.pkl        # per-motion metadata (lengths, contact flags, source IDs)
│   ├── object_usd/<hoi_category>__<object>__<NNN>.usd  # OpenUSD object asset
│   │   └── textures/<basename>/                        # per-USD texture subdir, refs rewritten in the USD
└── checkpoint/
    ├── GEM-SMPL/                                       # SMPL-X human pose estimation weights (HMR2, ViTPose, VIMO, YOLO, HMR4D)
    ├── FoundationPose/weights/                         # object 6-DoF estimator (refiner + scorer)
    └── SONIC/models/                                   # SONIC tracking checkpoints

The 3-digit NNN index restarts at 0 within each <object>.

Dataset Statistics per HOI Category:

Category	What it is	3D asset source	# objects	# motions	Seq. length	Total frames
`pickup_table`	Tabletop pick-up — grasp an object from a table surface, lift, transport	RoboCasa-derived 3D assets	685	2,991	10 s	747,750
`pickup_ground`	Ground pick-up — kneel to grasp an object from the floor and stand back up	RoboCasa-derived 3D assets	631	1,613	15 s	611,625
`sitting`	Sitting — sit down on chair-like objects	Hunyuan3D-generated 3D assets	189	1,748	5 s	218,500
`slope`	Terrain slope locomotion — walk over inclined surfaces	Procedural terrain assets	200	1,880	10 s	470,000
`curb`	Terrain curb locomotion — step over curb assets	Procedural terrain assets	200	1,769	10 s	442,250
`stair` (`stair_p1` + `stair_p2`)	Stair locomotion — ascend and descend stair assets	Generated synthetic and real stair assets	4,952	12,188	10 s	3,047,000

Data Visualization:

Released data can be rendered into kinematic-replay MP4s using GRAIL data visualization. The output can then be browsed using GRAIL web visualizer for hover-to-play previews.

git clone https://github.com/NVlabs/GRAIL.git && cd GRAIL
# follow the installation guide to set up the `sonic` conda env
conda activate sonic
export DISPLAY=:1

# Batch — render up to 16 motions (default), defaults match the release (xyzw quats)
bash grail/visualization/scripts/visualize.sh \
    data/pickup_table

# Or render a single motion from its robot/<seq>.pkl
bash grail/visualization/scripts/visualize_single.sh \
    data/pickup_table/robot/pickup_table__apple_0__000.pkl

Data Collection Method:

Hybrid — Automatic. Each motion is the deterministic output of the GRAIL pipeline:

3D asset acquisition — RoboCasa-derived meshes, AI-generated meshes from Hunyuan3D-2.1, or procedural terrain assets.
2D HOI generation — a Blender rendering + video generated through Kling AI.
4D HOI reconstruction — SMPL-X body pose recovered via GEM-SMPL; object 6-DoF via FoundationPose.
Retargeting — SMPL-X human pose is retargeted to the Unitree G1 skeleton via GMR engine.
Task general tracking — the retargeted motion is used as a tracking reference for a SONIC policy in Isaac Lab.

Explainability and Safety

GRAIL trajectories are synthetic. No real individuals appear in the source videos, SMPL-X reconstructions, or any other modality. Users deploying derived policies on physical humanoids are responsible for hardware-side safety review (joint-limit / torque / E-stop / human-proximity safeguards) prior to deployment.

For full details on ethical considerations, technical limitations, and security considerations (including the use of pickled files), please refer to the "Explainability" and "Safety" sections within the documentation.

Citation

@misc{grail2026,
  title         = {GRAIL: Generating Humanoid Loco-Manipulation from 3D Assets and Video Priors},
  author        = {Tianyi Xie and Haotian Zhang and Jinhyung Park and Zi Wang and Bowen Wen and Jiefeng Li and Xueting Li and Qingwei Ben and Haoyang Weng and Yufei Ye and David Minor and Tingwu Wang and Chenfanfu Jiang and Sanja Fidler and Jan Kautz and Linxi Fan and Yuke Zhu and Zhengyi Luo and Umar Iqbal and Ye Yuan},
  year          = {2026},
  eprint        = {2606.05160},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  doi           = {10.48550/arXiv.2606.05160},
  url           = {https://arxiv.org/abs/2606.05160},
}