Update dataset card: eval-assets, download commands, provenance

README.md

@@ -14,28 +14,68 @@ pretty_name: SoftVTBench
 
 # SoftVTBench
 
-Visuo-tactile manipulation data for rigid and soft/deformable LIBERO-style pick-and-place tasks, collected with a tactile-sensing Franka arm in Isaac Lab (Tabero simulation stack). Companion data release for [SoftTacWorld](https://github.com/) (code repository).
+Visuo-tactile manipulation data for **rigid and soft/deformable** LIBERO-style
+pick-and-place tasks, collected with a tactile-sensing Franka arm in Isaac Lab
+(Tabero simulation stack).
+
+Mirrored on both hubs:
+
+- Hugging Face — [`Arthur12137/SoftVTBench`](https://huggingface.co/datasets/Arthur12137/SoftVTBench)
+- ModelScope — [`Arthur12137/SoftVTBench`](https://www.modelscope.cn/datasets/Arthur12137/SoftVTBench)
+
+## Download
+
+```bash
+pip install -U huggingface_hub
+huggingface-cli download Arthur12137/SoftVTBench \
+  --repo-type dataset --local-dir ./SoftVTBench_data
+```
+
+From ModelScope (faster in mainland China):
+
+```python
+from modelscope import dataset_snapshot_download
+dataset_snapshot_download('Arthur12137/SoftVTBench', local_dir='./SoftVTBench_data')
+```
+
+The full release is ~2.3 GB. Fetch only what you need:
+
+```bash
+# training on the deformable-object suite
+huggingface-cli download Arthur12137/SoftVTBench --repo-type dataset \
+  --include 'object-soft/*' --local-dir ./SoftVTBench_data
+
+# add closed-loop evaluation (USD scene assets)
+huggingface-cli download Arthur12137/SoftVTBench --repo-type dataset \
+  --include 'eval-assets/*' --local-dir ./SoftVTBench_data
+```
 
 ## Folders
 
-| Folder | Task suite | Object type | Tasks x Demos | Size |
-|---|---|---|---|---|
-| `spatial-rigid/` | `libero_spatial` (baseline) | Rigid | 10 tasks, 207 demos (uneven) | 238M |
-| `spatial-soft/` | `libero_spatial` + soft pastry variant | Deformable | 10 tasks x 50 = 500 demos | 498M |
-| `object-rigid/` | `libero_object` (baseline) | Rigid | 10 tasks, 421 demos (uneven) | 425M |
-| `object-soft/` | `libero_object` + soft pastry variant (10 assets) | Deformable | 10 tasks x 50 = 500 demos | 896M |
-| `soft-assets/` | — | — | 11 deformable pastry USD assets + geometry primitives | 51M |
+| Folder | Task suite | Object type | Tasks × Demos | Size | Needed for |
+|---|---|---|---|---|---|
+| `object-soft/` | `libero_object` + soft pastry (10 assets) | Deformable | 10 × 50 = 500 | 896M | training, eval |
+| `spatial-soft/` | `libero_spatial` + soft pastry | Deformable | 10 × 50 = 500 | 498M | training, eval |
+| `object-rigid/` | `libero_object` (baseline) | Rigid | 10 tasks, 421 demos (uneven) | 425M | training, eval |
+| `spatial-rigid/` | `libero_spatial` (baseline) | Rigid | 10 tasks, 207 demos (uneven) | 238M | training, eval |
+| `eval-assets/` | — | — | 43 USD assets | 211M | **evaluation only** |
+| `soft-assets/` | — | — | 11 pastry USD + geometry primitives | 51M | asset authoring |
 
-`*-rigid` folders are baseline LIBERO replays (no soft-body assets); `*-soft` folders swap in deformable pastry objects and add FEM soft-body observations. See `soft-assets/configs/asset_manifest.csv` for the pastry asset list.
+`*-rigid` folders are baseline LIBERO replays (no soft-body assets); `*-soft`
+folders swap in deformable pastry objects and add FEM soft-body observations.
 
-## Format
+`eval-assets/` holds the USD scene library Isaac Sim needs to rebuild the scene
+for closed-loop evaluation — 32 LIBERO scene objects plus 11 deformable assets,
+all following the `<name>/<name>.usd` convention. Training does not need it.
+See `eval-assets/README.md` for provenance.
 
-Each `spatial-*`/`object-*` folder follows the "Replay HDF5" layout:
+## Layout
 
 ```text
-libero_{spatial,object}/
-  libero_{spatial,object}_task{N}/
-    replayed_demos/*.hdf5          # one file per task, all demos under data/demo_*
+object-soft/
+  manifest.jsonl
+  libero_object/libero_object_task{0..9}/
+    replayed_demos/*.hdf5                  # one file per task, demos under data/demo_*
     video_datasets/*/videos/*.mp4          # agentview + eye-in-hand RGB
     video_datasets/*/tactile_outputs/*.mp4 # rendered tactile marker video
 ```
@@ -47,13 +87,26 @@ Inside each HDF5, `data/demo_N` contains:
 - `obs/fem_deformation_max`, `obs/fem_deformation_rms`, `obs/fem_bbox_dims` — soft-body only, `*-soft` folders
 - `initial_state/`, `states/` — full Isaac Lab scene state for reset/replay
 
-Full field-by-field schema: see `datasets/schemas/*.md` in the SoftTacWorld code repository.
+Training pipelines drop the two force channels and supervise 7D actions.
 
 ## Known caveats
 
-- `spatial-rigid` / `object-rigid` demo counts are uneven per task (raw collection yield, not padded to a fixed quota).
-- `spatial-soft` force labels are mostly gripper-closing proxy forces rather than clean contact-sensor forces; task5 mixes both. See `spatial-soft/spatial_pastry005_data_quality_check_20260625.md`.
+- `spatial-rigid` / `object-rigid` demo counts are uneven per task (raw collection
+  yield, not padded to a fixed quota).
+- `spatial-soft` force labels are mostly gripper-closing proxy forces rather than
+  clean contact-sensor forces; task5 mixes both. See
+  `spatial-soft/spatial_pastry005_data_quality_check_20260625.md`.
+- `spatial-soft/manifest.jsonl` covers tasks 0–4 only; tasks 5–9 are listed in
+  `manifest_task5_9_copy_20260624.jsonl`. Derive demo counts from the HDF5 files,
+  not from a manifest line count.
+
+## Provenance & licensing
+
+Released under the Apache License 2.0.
 
-## License
+The rigid scene objects in `eval-assets/` originate from the
+[LIBERO](https://github.com/Lifelong-Robot-Learning/LIBERO) benchmark (MIT),
+converted to USD by [Tabero](https://github.com/NathanWu7/Tabero) (Apache-2.0).
+The deformable assets and all trajectory data are contributed by this project.
 
-Apache License 2.0.
+If you use this dataset, please cite LIBERO and Tabero alongside SoftVTBench.