Add robotics task category, sample usage and citation

README.md

@@ -1,19 +1,25 @@
 ---
 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
+- 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**](https://research.nvidia.com/labs/dair/grail/) | [**Paper**](https://arxiv.org/abs/2606.05160) | [**Code**](https://github.com/NVlabs/GRAIL) | [**Docs**](https://nvlabs.github.io/GRAIL/)
+
 # Dataset Overview
 
 | Tabletop Pickup | Ground Pickup |
@@ -32,7 +38,7 @@ library_name: GRAIL
 |:---:|:---:|
 | <img src="assets/videos/terrain_slopes.gif" width="420"/> | <img src="assets/videos/terrain_stairs.gif" width="420"/> |
 
-###  Description:
+### 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.
 
@@ -40,7 +46,25 @@ The release is partitioned by HOI category. Each motion ships with: the source s
 
 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).
 
-##  License/Terms of Use:
+## 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](https://github.com/NVlabs/GRAIL) repository):
+
+```bash
+# 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:
 
@@ -56,7 +80,7 @@ Object USD assets under `data/<hoi_category>/object_usd/` come from four asset s
 
 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:
+## 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:
 
@@ -64,14 +88,14 @@ GRAIL is intended for use by individuals and professionals in fields such as rob
 * **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):
+## 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:
+## Dataset Layout:
 
 ```
 nvidia/PhysicalAI-Robotics-Locomanipulation-GRAIL/
@@ -91,7 +115,7 @@ nvidia/PhysicalAI-Robotics-Locomanipulation-GRAIL/
 
 The 3-digit `NNN` index restarts at 0 within each `<object>`.
 
-###  Dataset Statistics per HOI Category:
+### Dataset Statistics per HOI Category:
 
 | Category | What it is | 3D asset source | # objects | # motions | Seq. length | Total frames |
 |---|---|---|---:|---:|---:|---:|
@@ -102,20 +126,7 @@ The 3-digit `NNN` index restarts at 0 within each `<object>`.
 | `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 |
 
-Additional categories (tabletop / ground manipulation) are planned for subsequent releases.
-
-##  Additional Statistics:
-
-| Field | Value |
-|---|---|
-| Synthetic video fps | 24 fps |
-| Reconstructed motion fps | 24 fps |
-| Released trajectory rate | 25 Hz |
-| Reconstructed body model | SMPL-X (75 body DOFs + 45 × 2 hand DOFs) |
-| Robot platform | Unitree G1 (29 body DOFs + 7 × 2 hand DOFs) |
-| Modalities per motion | Video (mp4), 4D HOI recon (pkl), robot traj (pkl), object traj (pkl), meta (pkl), object asset (USD + textures) |
-
-##  Data Visualization:
+## Data Visualization:
 
 Released data can be rendered into kinematic-replay MP4s using [GRAIL data visualization](https://NVlabs.github.io/GRAIL/visualization.html). The output can then be browsed using [GRAIL web visualizer](https://NVlabs.github.io/GRAIL/web_visualizer.html) for hover-to-play previews.
 
@@ -134,67 +145,33 @@ bash grail/visualization/scripts/visualize_single.sh \
     data/pickup_table/robot/pickup_table__apple_0__000.pkl
 ```
 
-Outputs land alongside the motion library so the same directory works as input to the web visualizer:
-
-```
-data/<hoi_category>/vis/
-├── <seq>.mp4                    one per motion
-├── all_motions_combined.mp4     concat (only when --max_videos = 0)
-└── examples_grid.mp4            4×4 or 2×2 grid (only when --max_videos = 0)
-```
-
-Knobs you may want to set:
-
-| Arg / env | Default | Notes |
-|---|---|---|
-| `max_videos` (2nd positional) | `16` | Cap on motions rendered. Pass `0` to render the whole library and produce the concat / grid MP4s. |
-| `cam_offset` (3rd positional) | `1.5,-1.5,1.0` | Camera position relative to the motion centroid. |
-
-##  Data Collection Method:
+## Data Collection Method:
 
 Hybrid — Automatic. Each motion is the deterministic output of the GRAIL pipeline:
 
-1. **3D asset acquisition** — RoboCasa-derived meshes, AI-generated meshes from Hunyuan3D-2.1, or procedural terrain assets. No real-world scans of identifiable objects.
-2. **2D HOI generation** — a Blender rendering places a SMPL-X-rigged character with the object in an synthetic scene; a video exhibiting character-object interaction is generated through Kling AI.
-3. **4D HOI reconstruction** — SMPL-X body pose recovered via GEM-SMPL (HMR2 + ViTPose + VIMO + HMR4D); object 6-DoF via FoundationPose conditioned on a SAM2 mask and a MoGe depth prior; jointly optimized in a multi-stage HOI optimizer.
-4. **Retargeting** — SMPL-X human pose is retargeted to the Unitree G1 skeleton via the [GMR](https://github.com/YanjieZe/GMR) IK + temporal-smoothing engine. Hand DOFs and per-motion USD assets are assembled in the same pass.
-5. **Task general tracking** — the retargeted motion is used as a tracking reference for a SONIC policy in Isaac Lab. The post-RL object trajectory is the one realized by the simulated G1 + object under contact dynamics — guaranteed to be physically feasible by construction.
-
-##  Disclaimer
-
-NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this dataset and the downstream models trained on it meet requirements for the relevant industry and use case and addresses unforeseen product misuse.
-
-##  Ethical Considerations:
-
-GRAIL trajectories are synthetic. No real individuals appear in the source videos, SMPL-X reconstructions, or any other modality — the entire pipeline is synthetic-character-only (the body model is parametric SMPL-X driven by retargeted character animation; no real-person mocap appears in the released motions). The 3D objects are AI-generated, procedurally generated, or licensed from synthetic asset libraries.
-
-Users training policies on GRAIL are responsible for the safety properties of those policies once deployed on physical humanoids; the dataset itself is a kinematic reference and does not encode safety constraints, controller stability margins, or hardware torque/velocity envelopes. The Unitree G1 trajectories are guaranteed physically feasible in the Isaac Lab simulation environment under the SONIC tracker — sim-to-real transfer requires additional validation by the integrating team.
+1. **3D asset acquisition** — RoboCasa-derived meshes, AI-generated meshes from Hunyuan3D-2.1, or procedural terrain assets.
+2. **2D HOI generation** — a Blender rendering + video generated through Kling AI.
+3. **4D HOI reconstruction** — SMPL-X body pose recovered via GEM-SMPL; object 6-DoF via FoundationPose.
+4. **Retargeting** — SMPL-X human pose is retargeted to the Unitree G1 skeleton via GMR engine.
+5. **Task general tracking** — the retargeted motion is used as a tracking reference for a SONIC policy in Isaac Lab.
 
-For more detailed information on ethical considerations for the upstream models GRAIL builds on, see the corresponding model cards: [`nvidia/GEM-X`](https://huggingface.co/nvidia/GEM-X) (human pose estimation) and the FoundationPose project page.
+## Explainability and Safety
 
-##  Explainability
+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. 
 
-| Field | Response |
-|---|---|
-| Intended Task/Domain: | Humanoid whole-body loco-manipulation — supervising RL or imitation-learning controllers on physically validated reference motions for the Unitree G1. |
-| Dataset Type: | Trajectory dataset (per-motion robot + object 6-DoF + source video + SMPL-X recon). |
-| Intended Users: | Robotics learning researchers; machine-learning engineers; humanoid control researchers; computer-vision and graphics researchers working on HOI. |
-| Output: | Per-motion: G1 robot trajectory `(T, 29)` + hand DOFs, object 6-DoF `(T, 7)` (xyz + quat), input video (mp4), 4D HOI recon (SMPL-X parameters + object pose, world frame), per-motion metadata (`meta/*.pkl`), object asset (`*.usd` + textures). |
-| Describe how the dataset was produced: | A five-stage automated pipeline. (1) 3D asset acquisition; (2) character-object interaction rendered in Blender + video generation via Kling-AI; (3) 4D HOI reconstruction — SMPL-X via GEM-SMPL, object 6-DoF via FoundationPose; joint multi-stage optimizer; (4) retargeting to Unitree G1 via GMR; (5) task general tracking — the retargeted motion drives a SONIC policy in Isaac Lab, and the released `robot/` and `objects/` trajectories are what the simulated G1 + object actually realize under contact dynamics. |
-| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not formally tested across demographic subgroups. Because the released trajectories are in G1 joint space rather than per-character body space, per-group outcome variation does not propagate to the dataset's primary downstream use (controller training). |
-| Technical Limitations & Mitigation: | (1) **Single robot platform** — G1 only; cross-embodiment retargeting requires additional work. (2) **Synthetic-to-real domain gap** — source videos are synthetic, so visual-feature-based downstream use (e.g. vision-conditioned policies) may need real-video fine-tuning. (3) **No tactile / force annotations** — the dataset is kinematic + 6-DoF only; contact forces are not exposed. | 
-| Verified to have met prescribed NVIDIA quality standards: | Yes. |
-| Performance Metrics: | Per-motion physical feasibility is verified by construction (released motions are by definition those a trained SONIC tracker can follow). |
-| Potential Known Risks: | (1) **Sim-to-real assumption mismatch** — trajectories that succeed in Isaac Lab may not succeed on a physical G1 without additional residual learning, system-identification, or torque-limit checks. (2) **Object-asset license inheritance** — released USD assets and source videos inherit the license of their upstream source (RoboCasa, ComAsset, or Hunyuan3D); downstream users should confirm any application-specific redistribution constraints — see the License section above. |
-| Licensing: | The dataset itself is released under [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). Bundled checkpoints, RoboCasa-derived object assets, and ComAsset-derived object assets retain their respective upstream licenses — see the **License / Terms of Use** section above for the full per-subtree breakdown. |
+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.
 
-##  Safety
+## Citation
 
-| Field | Response |
-|---|---|
-| Dataset Application Field(s): | Robotics Learning; Humanoid Whole-Body Control; Loco-Manipulation Research; Physics-Based Animation; Sim-to-Real Transfer Research. |
-| Describe the life critical impact (if present). | Not Applicable for direct dataset use. Downstream policies trained on GRAIL motions and deployed on a physical Unitree G1 are operating a hardware actuator and require independent safety validation by the integrating team (torque / velocity limit checks, emergency-stop integration, environment-specific risk assessment). The dataset itself does not encode hardware safety constraints. |
-| Use Case Restrictions: | Abide by the per-subtree licenses documented in the **License / Terms of Use** section above. In addition, GRAIL must not be used to: (1) train policies for deployment on humanoids in public-facing safety-critical roles (e.g. interacting with vulnerable populations) without independent safety validation; (2) produce or distribute deepfake content of real individuals — the dataset contains no real-person data, so any such use would require off-pipeline data that violates the upstream synthesis constraints; (3) violate the licenses of upstream third-party assets (object meshes, character libraries, motion sources). |
-| Dataset restrictions: | The Principle of Least Privilege (PoLP) is applied. Source assets (RoboCasa-derived 3D objects, synthetic character library, motion-source mocap) are tracked with NSpect IDs in the upstream pipelines. The release pipeline strips path-level provenance from the original cluster filesystems before publication. |
-| Security considerations: | The dataset is composed of pickled trajectories (Python `.pkl`), MP4 videos, OpenUSD assets, and a CSV manifest. Pickled files execute arbitrary Python on load; users should verify the integrity of downloaded files (HF hashes / commit signatures) and load them inside a trusted environment. The release ships no executable code, no model weights with auto-execute capability, and makes no network calls when loaded. Report security vulnerabilities to NVIDIA [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). |
-| Responsible AI practices: | GRAIL is designed to advance public research on humanoid loco-manipulation. 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. NVIDIA encourages developers to implement validation harnesses, sim-to-real gap analysis, and conservative envelope checks in any pipeline that takes a GRAIL-trained policy to hardware. |
+```bibtex
+@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},
+}
+```