Add dataset card

README.md

@@ -1,8 +1,5 @@
 ---
-license: other
-license_name: nvidia-open-model-license
-license_link: >-
-  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
+license: apache-2.0
 tags:
   - humanoid-locomanipulation
   - whole-body-control
@@ -37,8 +34,7 @@ library_name: GRAIL
 
 ###  Description:
 
-**GRAIL** (Generating Humanoid Loco-Manipulation from 3D
-Assets and Video Priors) is a dataset of physics-validated 4D human-object interaction (HOI) trajectories for **Unitree G1** humanoid robot. Each motion is the output of an end-to-end pipeline that (1) generates a synthetic interaction video from a 3D asset, (2) reconstructs the underlying 4D HOI (SMPL-X human pose + object 6-DoF) from that video, (3) retargets the human motion to the G1 skeleton, and (4) validates the trajectory in physics simulation by training a reinforcement-learning (RL) tracker against it — the released motion data is the output of the RL tracking policy.
+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).
 
@@ -46,7 +42,19 @@ The repo also ships the **submodule checkpoints** required to re-run the full GR
 
 ##  License/Terms of Use:
 
-Use of the released dataset is governed by the Apache License, Version 2.0. Use of the associated checkpoint weights is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).
+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](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).
+- **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 and source videos under `data/<hoi_category>/{object_usd,video}/` come from four asset sources, each with different downstream-license implications:
+
+- **RoboCasa-derived** (`pickup_table`, `pickup_ground`) — derivative works of [RoboCasa](https://robocasa.ai/) (UT Austin, NVIDIA), licensed under [CC BY 4.0](https://creativecommons.org/licenses/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](https://huggingface.co/datasets/SShowbiz/ComAsset) (Kim et al., ECCV 2024), licensed under [ODC-By v1.0](https://opendatacommons.org/licenses/by/1-0/). 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.
+- **Procedurally generated** (`curb`, `slope`, `stairs`) — GRAIL-original outputs, released under Apache License 2.0.
+- **Hunyuan3D-2.1 generated** (`advanced manipulation`, `stairs`) — outputs of [Hunyuan3D-2.1](https://github.com/Tencent-Hunyuan/Hunyuan3D-2.1). Per the [Tencent Hunyuan 3D 2.1 Community License](https://github.com/Tencent-Hunyuan/Hunyuan3D-2.1/blob/main/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.
+
+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:
 
@@ -77,7 +85,8 @@ nvidia/PhysicalAI-Robotics-Locomanipulation-GRAIL/
 │   │   └── 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)
+    ├── FoundationPose/weights/                         # object 6-DoF estimator (refiner + scorer)
+    └── SONIC/models/                                   # SONIC tracking checkpoints
 ```
 
 The 3-digit `NNN` index restarts at 0 within each `<object>`.
@@ -104,7 +113,7 @@ Additional categories (tabletop / ground manipulation) are planned for subsequen
 
 ##  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.
+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.
 
 ```bash
 git clone https://github.com/NVlabs/GRAIL.git && cd GRAIL
@@ -142,7 +151,7 @@ Knobs you may want to set:
 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 simulation places a SMPL-X-rigged character interacting with the object; the rendered video is passed through Kling AI to add photorealistic appearance.
+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.
@@ -153,20 +162,12 @@ NVIDIA believes Trustworthy AI is a shared responsibility and we have establishe
 
 ##  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, procedural, or licensed from synthetic asset libraries (e.g. RoboCasa).
+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.
+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.
 
 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.
 
-##  Bias
-
-| Field | Response |
-|---|---|
-| Participation considerations from adversely impacted groups in dataset design and testing: | The dataset contains no real individuals. The synthetic 3D characters used to render source videos are drawn from a finite library; their demographic diversity — skin tone, body proportions, age group, sex — is bounded by that library and may not proportionally represent all global populations. Body proportions and appearances common in East Asian, South Asian, Sub-Saharan African, and other underrepresented groups may be less prevalent in the synthetic distribution. The downstream G1 retargeting collapses character-specific body shape onto a single G1 skeleton, so identity-level appearance variation does not propagate into the released robot trajectories. No adversely impacted groups were formally consulted during dataset design or testing. |
-| Measures taken to mitigate against unwanted bias: | (1) **Identity-agnostic output**: The released robot trajectories are encoded in the G1 joint space, which is appearance- and identity-free by construction; (2) **Motion diversity decoupled from appearance**: each character drives a wide variety of motions sampled across HOI categories; (3) **No real-video sampling bias**: the source videos are fully synthetic, so the dataset does not inherit demographic biases from internet-scraped video corpora. |
-| Bias Metric (If Measured): | No formal demographic bias metric has been measured for the dataset. |
-
 ##  Explainability
 
 | Field | Response |
@@ -175,31 +176,13 @@ For more detailed information on ethical considerations for the upstream models
 | 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 six-stage automated pipeline. (1) 3D asset acquisition — RoboCasa / Hunyuan3D / procedural; (2) physics-simulated character-object interaction in Blender → frame-by-frame render; (3) Kling-AI re-render for photorealistic appearance; (4) 4D HOI reconstruction — SMPL-X via GEM-SMPL, object pose via FoundationPose; joint multi-stage optimizer; (5) retargeting to Unitree G1 via GMR; (6) RL validation in Isaac Lab using SONIC. |
+| 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 inherit the license of their upstream source (RoboCasa / Hunyuan3D); downstream users should confirm any application-specific redistribution constraints. |
-| Licensing: | Use of the source code is governed by the [Apache License, Version 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md). Use of the associated dataset is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). |
-
-##  Privacy
-
-| Field | Response |
-|---|---|
-| Generatable or reverse engineerable personal data? | No. The dataset contains no real-person data at any stage — input videos are synthetic renders of synthetic characters; reconstructions are SMPL-X parameters of those synthetic characters; the released robot trajectories are in Unitree G1 joint space and encode no identity, appearance, or biometric signal. |
-| Personal data used to create this dataset? | No. The upstream GEM-SMPL human-pose checkpoints were trained on internally generated synthetic video data (see the [`nvidia/GEM-X`](https://huggingface.co/nvidia/GEM-X) model card). Motion capture animations used during character rigging upstream are recorded from real performers but were retargeted to fixed skeletons that strip identity-specific biometric signals. |
-| Was consent obtained for any personal data used? | Yes (upstream — see `nvidia/GEM-X` for the consent chain on upstream mocap). |
-| Description of methods implemented in data acquisition or processing, if any, to address the prevalence of personal data in the training data: | The release pipeline is end-to-end synthetic. The source character meshes (RenderPeople / NVIDIA Digital Humans / RoboCasa-derived avatars) are synthetic. Re-renders via Kling AI are conditioned on synthetic frames, not real footage. No real-world camera capture is performed at any pipeline stage. |
-| How often is dataset reviewed? | Datasets are initially reviewed upon addition; subsequent reviews are conducted as needed or upon request for changes. |
-| Is a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable — the dataset contains no real-person data. |
-| If personal data was collected for the development of the dataset, was it collected directly by NVIDIA? | No |
-| If personal data was collected for the development of the dataset by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable |
-| If personal data was collected for the development of this dataset, was it minimized to only what was required? | Yes. |
-| Was data from user interactions with the dataset (e.g. user input and prompts) used to extend the dataset? | No |
-| Does data labeling (annotation, metadata) comply with privacy laws? | Yes — all annotations are automatically generated from the synthetic rendering / reconstruction pipeline; no personal data is annotated. |
-| Is data compliant with data subject requests for data correction or removal, if such a request was made? | Not Applicable — no personal data. |
-| Applicable Privacy Policy | <https://www.nvidia.com/en-us/about-nvidia/privacy-policy/> |
+| 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. |
 
 ##  Safety
 
@@ -207,7 +190,7 @@ For more detailed information on ethical considerations for the upstream models
 |---|---|
 | 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 [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). 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). |
+| 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. |