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README.md

@@ -40,17 +40,13 @@ library_name: GRAIL
 **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.
 
-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). This layout is byte-compatible with the GRAIL training stack, so the release can be dropped into a `motion_lib_cfg.motion_file` / `object_motion_file` / `object_usd_path` slot without any conversion.
+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).
+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:
 
-Use of the source code is governed by the Apache License, Version 2.0. Use of the associated dataset and checkpoint weights is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).
-
-##  Deployment Geography:
-
-Global
+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 Case:
 
@@ -60,36 +56,28 @@ 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.
 
-##  Release Date:
-
-Hugging Face: 06/03/2026
-
 ##  Reference(s):
 
-* **GRAIL**: Generating Humanoid Loco-Manipulation from Video Foundation Models — The GRAIL Authors, 2026
-* **GENMO**: Generalist Model for Human Motion — Li et al., ICCV 2025 (used for SMPL-X / SOMA human pose estimation; `nvidia/GEM-X`)
-* **FoundationPose**: Wen et al., CVPR 2024 (used for object 6-DoF pose tracking)
-* **SMPL-X**: Pavlakos et al., CVPR 2019
 * Project page: <https://research-staging.nvidia.com/labs/dair/grail/>
 * Paper: <https://research-staging.nvidia.com/labs/dair/grail/static/pdf/grail_paper.pdf>
 * Code: <https://github.com/NVlabs/GRAIL>
-* Documentation: <https://nvlabs.github.io/GRAIL/>
+* Documentation: <https://NVlabs.github.io/GRAIL/>
 
 ##  Dataset Layout:
 
 ```
 nvidia/PhysicalAI-Robotics-Locomanipulation-GRAIL/
 ├── data/<hoi_category>/
-│   ├── recon/<hoi_category>__<object>__<NNN>.pkl       # 4D HOI recon (SMPL-X + object 6-DoF, world frame)
 │   ├── video/<hoi_category>__<object>__<NNN>.mp4       # source synthetic video
-│   ├── robot/<hoi_category>__<object>__<NNN>.pkl       # G1 robot trajectory (29 body DOFs + hand_dof_pos)
+│   ├── 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
+│   │   └── 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)
+    ├── GEM-SMPL/                                       # SMPL-X human pose estimation weights (HMR2, ViTPose, VIMO, YOLO, HMR4D)
+    └── FoundationPose/weights/                         # object 6-DoF estimator (refiner + scorer)
 ```
 
 The 3-digit `NNN` index restarts at 0 within each `<object>`.
@@ -101,7 +89,7 @@ The 3-digit `NNN` index restarts at 0 within each `<object>`.
 | `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 |
 
-Additional categories (tabletop / ground manipulation, sitting, terrain locomotion — curb / slope / stairs) are planned for subsequent releases.
+Additional categories (tabletop / ground manipulation) are planned for subsequent releases.
 
 ##  Additional Statistics:
 
@@ -114,22 +102,7 @@ Additional categories (tabletop / ground manipulation, sitting, terrain locomoti
 | 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 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 synthesis** — 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.
-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. **RL 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.
-
-##  Labeling Method:
-
-Automated. All annotations (SMPL-X body parameters, object 6-DoF, contact flags, per-frame joint positions) are produced by the GRAIL reconstruction stack; no manual labeling.
-
-##  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.
 
@@ -164,6 +137,16 @@ Knobs you may want to set:
 | `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:
+
+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.
+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.