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README.md

@@ -5,108 +5,135 @@ task_categories:
 tags:
   - manipulation
   - tactile
+  - visuo-tactile
   - deformable-objects
   - soft-body
   - libero
   - isaac-lab
 pretty_name: SoftVTBench
+size_categories:
+  - 1K<n<10K
 ---
 
 # 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).
+Visuo-tactile manipulation demonstrations 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
+Earlier releases (evaluation USD assets and the first partial data drops) were
+moved to `Arthur12137/SoftVTBench-archive` on both hubs.
 
-```bash
-pip install -U huggingface_hub
-huggingface-cli download Arthur12137/SoftVTBench \
-  --repo-type dataset --local-dir ./SoftVTBench_data
-```
+## Contents
 
-From ModelScope (faster in mainland China):
+Four subsets, each **10 tasks × 100 successful demonstrations = 1000 demos**
+and **4000 videos**.
 
-```python
-from modelscope import dataset_snapshot_download
-dataset_snapshot_download('Arthur12137/SoftVTBench', local_dir='./SoftVTBench_data')
-```
+| Subset | Scene / goal | Object | Video res. | Episode len | hdf5 | Videos | Total |
+|---|---|---|---|---|---|---|---|
+| `spatial-rigid` | kitchen table → plate | rigid pastry | 512×512 | 141–181 | 1250 MB | 560 MB | 1.8 GB |
+| `object-rigid` | floor → basket | rigid pastry | 512×512 | 107–130 | 475 MB | 482 MB | 975 MB |
+| `object-soft` | floor → basket | FEM soft body | 1024×1024 | 112–137 | 919 MB | 1074 MB | 2.0 GB |
+| `spatial-soft` | kitchen table → plate | FEM soft body | 1024×1024 | 120–144 | 1390 MB | 1138 MB | 2.5 GB |
 
-The full release is ~2.3 GB. Fetch only what you need:
+Every episode carries `success = True`; failures were filtered out at assembly
+time (the skipped records are kept in `failure.jsonl`).
 
-```bash
-# training on the deformable-object suite
-huggingface-cli download Arthur12137/SoftVTBench --repo-type dataset \
-  --include 'object-soft/*' --local-dir ./SoftVTBench_data
+## Layout
 
-# add closed-loop evaluation (USD scene assets)
-huggingface-cli download Arthur12137/SoftVTBench --repo-type dataset \
-  --include 'eval-assets/*' --local-dir ./SoftVTBench_data
+```
+<subset>/
+├── manifest.jsonl              # one line per demo: task_id, demo_id, language, num_samples,
+│                               # success, paths to the hdf5 and the 4 videos
+├── assemble_summary.json       # per-task counts produced by the assembler
+├── duplicate.jsonl             # empty in all four subsets
+├── failure.jsonl               # demos dropped during assembly (soft subsets only)
+└── libero_{spatial,object}/
+    └── libero_{spatial,object}_task{0..9}/
+        ├── replayed_demos/<task>_<language>_replayed_demo.hdf5     # 100 demos
+        └── video_datasets/<task>/
+            ├── videos/demo_<i>_agentview_rgb.mp4
+            ├── videos/demo_<i>_eye_in_hand_rgb.mp4
+            └── tactile_outputs/demo_<i>_gsmini_{left,right}_markers_rgb.mp4
 ```
 
-## Folders
+## HDF5 schema
+
+```
+data/demo_<i>/
+├── actions            (T, 13) float32     # full action record
+├── actions_binary     (T, 7)  float32     # 6-DoF delta + binary gripper
+├── obs/                                   # policy-facing observations
+├── soft_extras/                           # raw / extra channels used for analysis
+├── states/                                # per-step full simulator state
+└── initial_state/                         # reset state (unique per demo)
+```
 
-| 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 |
+`obs/` fields, all four subsets:
 
-`*-rigid` folders are baseline LIBERO replays (no soft-body assets); `*-soft`
-folders swap in deformable pastry objects and add FEM soft-body observations.
+`actions`, `applied_torque`, `arm_joint_pos`, `computed_torque`,
+`eef_axis_angle`, `eef_pose`, `fem_bbox_dims`, `finger_force`,
+`gripper_binary`, `gripper_close_norm`, `gripper_marker_motion`,
+`gripper_net_force`, `gripper_pos`, `gripper_width`
 
-`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.
+The two soft subsets add four deformation channels:
+`fem_deformation_max`, `fem_deformation_rms`,
+`fem_kabsch_max_pct`, `fem_kabsch_rms_pct`.
 
-## Layout
+`gripper_marker_motion` is the tactile signal: `(T, 2, 2, 99, 2)` — two GelSight
+Mini pads × (reference, current) × 99 markers × (x, y) displacement.
 
-```text
-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
-```
+Per-demo attributes include `asset_name`, `demo_id`, `language`, `num_samples`,
+`success`, `soft_task_kind`, `task_id`, `task_suite`, and a `metadata_json`
+blob with the sampled gripper closure.
 
-Inside each HDF5, `data/demo_N` contains:
+## Verified integrity
 
-- `actions` — (T, 13) float32: xyz(3) + axis-angle(3) + abs gripper(1) + left/right force(3+3)
-- `obs/eef_pose`, `obs/gripper_pos`, `obs/arm_joint_pos`, `obs/gripper_marker_motion`, `obs/gripper_net_force`
-- `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
+Checked over the full release, not sampled unless noted:
 
-Training pipelines drop the two force channels and supervise 7D actions.
+- demo count = manifest lines = `success=True` count = 1000 per subset
+- 4 videos per demo, no zero-byte or truncated files
+- every path referenced by `manifest.jsonl` exists
+- video frame count equals hdf5 episode length (360 pairs sampled)
+- no NaN/Inf in any float field; all `obs/` fields aligned with `actions`
+- 1000/1000 unique action trajectories and 1000/1000 unique `initial_state`
+  per subset — the per-demo randomisation (object XY, goal XY, robot joints,
+  gripper closing force) is genuinely distinct
 
 ## 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-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.
+- **Video resolution differs**: rigid subsets are 512×512, soft subsets 1024×1024.
+- **`demo_id` conventions differ** across subsets (9-digit global ids in
+  `spatial-rigid`/`object-rigid`, 5–6-digit in `object-soft`, 0–350 in
+  `spatial-soft`). In `spatial-soft` the id is only unique *within* a task.
+  Index by `(subset, task_id, demo)` when merging subsets.
+- **HDF5 compression is not uniform**: the two `object-*` subsets were written
+  with gzip, the two `spatial-*` subsets uncompressed. This is why
+  `object-rigid` is much smaller on disk despite holding the same amount of data.
+- **`gripper_marker_motion` is stored twice**, identically, under both `obs/`
+  and `soft_extras/`; it accounts for ~40% of each file.
+- **Task 6 language is a placeholder** (`"golden pastry"`) in `object-rigid`
+  and `object-soft`.
+- Paths inside `manifest.jsonl` are relative to the original collection root
+  (`outputs/...`), not to this repository.
+
+## Download
 
-## Provenance & licensing
+```bash
+pip install -U huggingface_hub
+hf download Arthur12137/SoftVTBench --repo-type dataset --local-dir ./SoftVTBench
+```
 
-Released under the Apache License 2.0.
+```bash
+pip install -U modelscope
+modelscope download --dataset Arthur12137/SoftVTBench --local_dir ./SoftVTBench
+```
 
-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.
+## License
 
-If you use this dataset, please cite LIBERO and Tabero alongside SoftVTBench.
+Apache-2.0.

object-rigid/assemble_summary.json

@@ -0,0 +1,22 @@
+{
+  "out_root": "outputs/object_rigid",
+  "runs_roots": [
+    "outputs/Formal_collection_object_rigid"
+  ],
+  "skipped_duplicates": 0,
+  "skipped_failures": 0,
+  "task_counts": {
+    "0": 100,
+    "1": 100,
+    "2": 100,
+    "3": 100,
+    "4": 100,
+    "5": 100,
+    "6": 100,
+    "7": 100,
+    "8": 100,
+    "9": 100
+  },
+  "total_success": 1000,
+  "total_videos": 4000
+}

object-soft/assemble_summary.json

@@ -0,0 +1,22 @@
+{
+  "out_root": "outputs/object_softbody_assembled",
+  "runs_roots": [
+    "outputs/COLLECT_V1"
+  ],
+  "skipped_duplicates": 0,
+  "skipped_failures": 244,
+  "task_counts": {
+    "0": 100,
+    "1": 100,
+    "2": 100,
+    "3": 100,
+    "4": 100,
+    "5": 100,
+    "6": 100,
+    "7": 100,
+    "8": 100,
+    "9": 100
+  },
+  "total_success": 1000,
+  "total_videos": 3984
+}

object-soft/epsilon_reconstruction.json

@@ -0,0 +1,82 @@
+{
+  "pastry001": {
+    "d_safe": 0.1248,
+    "n": 100,
+    "mean": 0.4428880383580384,
+    "max": 0.9919320501789972,
+    "p90": 0.8969914338926633,
+    "over": 100
+  },
+  "pastry002": {
+    "d_safe": 0.0711,
+    "n": 100,
+    "mean": 0.4545346142465477,
+    "max": 0.9530007334760927,
+    "p90": 0.4918878341052838,
+    "over": 100
+  },
+  "pastry003": {
+    "d_safe": 0.0406,
+    "n": 100,
+    "mean": 0.26781459068219177,
+    "max": 0.34144518569961574,
+    "p90": 0.3249491680310657,
+    "over": 100
+  },
+  "stw_cube_hq": {
+    "d_safe": 0.1571,
+    "n": 100,
+    "mean": 0.31537962781429985,
+    "max": 0.46812187290686275,
+    "p90": 0.4256046431052158,
+    "over": 99
+  },
+  "pastry005": {
+    "d_safe": 0.1367,
+    "n": 100,
+    "mean": 0.4140072215824477,
+    "max": 0.48221792687123843,
+    "p90": 0.4684975714968116,
+    "over": 100
+  },
+  "stw_cylinder_hq": {
+    "d_safe": 0.0834,
+    "n": 100,
+    "mean": 0.3362182298297621,
+    "max": 0.533566428185391,
+    "p90": 0.4693884316863884,
+    "over": 100
+  },
+  "pastry011": {
+    "d_safe": 0.0589,
+    "n": 100,
+    "mean": 0.4978579557753095,
+    "max": 0.9992645219455744,
+    "p90": 0.9327867627271401,
+    "over": 100
+  },
+  "stw_cuboid_hq": {
+    "d_safe": null,
+    "n": 100,
+    "mean": 0.2312375468017527,
+    "max": 0.3570150916948424,
+    "p90": 0.3177432545739713,
+    "over": null
+  },
+  "stw_sphere_hq": {
+    "d_safe": 0.0355,
+    "n": 100,
+    "mean": 0.26588803666182254,
+    "max": 0.46744716143085685,
+    "p90": 0.41343776583993663,
+    "over": 100
+  },
+  "pastry010": {
+    "d_safe": 0.0502,
+    "n": 100,
+    "mean": 0.3094436392300135,
+    "max": 0.9996882555182994,
+    "p90": 0.3861238148659094,
+    "over": 100
+  }
+}

object-soft/epsilon_reconstruction_v2.json

@@ -0,0 +1,82 @@
+{
+  "pastry001": {
+    "d_safe": 0.1248,
+    "n": 100,
+    "mean": 0.2810754964908283,
+    "p90": 0.3641279214020167,
+    "max": 0.39420005682010284,
+    "over": 99
+  },
+  "pastry002": {
+    "d_safe": 0.0711,
+    "n": 100,
+    "mean": 0.4448374132154628,
+    "p90": 0.47741379895674574,
+    "max": 0.5394062398836798,
+    "over": 100
+  },
+  "pastry003": {
+    "d_safe": 0.0406,
+    "n": 100,
+    "mean": 0.26704469413765103,
+    "p90": 0.32433327626324937,
+    "max": 0.3406029054161705,
+    "over": 100
+  },
+  "stw_cube_hq": {
+    "d_safe": 0.1571,
+    "n": 100,
+    "mean": 0.303520819321062,
+    "p90": 0.41574694343809093,
+    "max": 0.4641996316576378,
+    "over": 96
+  },
+  "pastry005": {
+    "d_safe": 0.1367,
+    "n": 100,
+    "mean": 0.4135552634259789,
+    "p90": 0.4684975714968116,
+    "max": 0.48221792687123843,
+    "over": 100
+  },
+  "stw_cylinder_hq": {
+    "d_safe": 0.0834,
+    "n": 100,
+    "mean": 0.3235845229198941,
+    "p90": 0.4476036611665962,
+    "max": 0.5074430971509287,
+    "over": 100
+  },
+  "pastry011": {
+    "d_safe": 0.0589,
+    "n": 100,
+    "mean": 0.23016614459038334,
+    "p90": 0.27320165380359596,
+    "max": 0.30504914365776276,
+    "over": 100
+  },
+  "stw_cuboid_hq": {
+    "d_safe": null,
+    "n": 100,
+    "mean": 0.2296139715045777,
+    "p90": 0.3175970089461804,
+    "max": 0.3483916522452072,
+    "over": null
+  },
+  "stw_sphere_hq": {
+    "d_safe": 0.0355,
+    "n": 100,
+    "mean": 0.2624275674001654,
+    "p90": 0.41337935413746807,
+    "max": 0.45656991077653836,
+    "over": 100
+  },
+  "pastry010": {
+    "d_safe": 0.0502,
+    "n": 100,
+    "mean": 0.28858557054110084,
+    "p90": 0.3835264750316964,
+    "max": 0.41934656487499505,
+    "over": 100
+  }
+}

object-soft/failure.jsonl

@@ -0,0 +1,244 @@
+{"demo_id": 10038, "language": "pick up the white swirled pastry and place it in the basket", "num_samples": 132, "source_hdf5": "outputs/COLLECT_V1/runs/object_soft_pastry001_demo10038_sim_20260718_210326/replayed_demos/libero_object_task0_demo_10038.hdf5", "success": false, "task_id": 0, "task_suite": "libero_object"}
+{"demo_id": 10045, "language": "pick up the white swirled pastry and place it in the basket", "num_samples": 128, "source_hdf5": "outputs/COLLECT_V1/runs/object_soft_pastry001_demo10045_sim_20260718_212142/replayed_demos/libero_object_task0_demo_10045.hdf5", "success": false, "task_id": 0, "task_suite": "libero_object"}
+{"demo_id": 10048, "language": "pick up the white swirled pastry and place it in the basket", "num_samples": 127, "source_hdf5": "outputs/COLLECT_V1/runs/object_soft_pastry001_demo10048_sim_20260718_212447/replayed_demos/libero_object_task0_demo_10048.hdf5", "success": false, "task_id": 0, "task_suite": "libero_object"}
+{"demo_id": 10088, "language": "pick up the white swirled pastry and place it in the basket", "num_samples": 128, "source_hdf5": "outputs/COLLECT_V1/runs/object_soft_pastry001_demo10088_sim_20260718_205937/replayed_demos/libero_object_task0_demo_10088.hdf5", "success": false, "task_id": 0, "task_suite": "libero_object"}
+{"demo_id": 10089, "language": "pick up the white swirled pastry and place it in the basket", "num_samples": 135, "source_hdf5": "outputs/COLLECT_V1/runs/object_soft_pastry001_demo10089_sim_20260718_210032/replayed_demos/libero_object_task0_demo_10089.hdf5", "success": false, "task_id": 0, "task_suite": "libero_object"}
+{"demo_id": 10095, "language": "pick up the white swirled pastry and place it in the basket", "num_samples": 129, "source_hdf5": "outputs/COLLECT_V1/runs/object_soft_pastry001_demo10095_sim_20260718_211703/replayed_demos/libero_object_task0_demo_10095.hdf5", "success": false, "task_id": 0, "task_suite": "libero_object"}
+{"demo_id": 10099, "language": "pick up the white swirled pastry and place it in the basket", "num_samples": 126, "source_hdf5": "outputs/COLLECT_V1/runs/object_soft_pastry001_demo10099_sim_20260718_212128/replayed_demos/libero_object_task0_demo_10099.hdf5", "success": false, "task_id": 0, "task_suite": "libero_object"}
+{"demo_id": 20000, "language": "pick up the white swirled pastry and place it in the basket", "num_samples": 134, "source_hdf5": "outputs/COLLECT_V1/runs/object_soft_pastry001_demo20000_sim_20260718_205907/replayed_demos/libero_object_task0_demo_20000.hdf5", "success": false, "task_id": 0, "task_suite": "libero_object"}
+{"demo_id": 20001, "language": "pick up the white swirled pastry and place it in the basket", "num_samples": 128, "source_hdf5": "outputs/COLLECT_V1/runs/object_soft_pastry001_demo20001_sim_20260718_210018/replayed_demos/libero_object_task0_demo_20001.hdf5", "success": false, "task_id": 0, "task_suite": "libero_object"}
+{"demo_id": 20007, "language": "pick up the white swirled pastry and place it in the basket", "num_samples": 134, "source_hdf5": "outputs/COLLECT_V1/runs/object_soft_pastry001_demo20007_sim_20260718_211949/replayed_demos/libero_object_task0_demo_20007.hdf5", "success": false, "task_id": 0, "task_suite": "libero_object"}
+{"demo_id": 20008, "language": "pick up the white swirled pastry and place it in the basket", "num_samples": 133, "source_hdf5": "outputs/COLLECT_V1/runs/object_soft_pastry001_demo20008_sim_20260718_212339/replayed_demos/libero_object_task0_demo_20008.hdf5", "success": false, "task_id": 0, "task_suite": "libero_object"}
+{"demo_id": 20012, "language": "pick up the white swirled pastry and place it in the basket", "num_samples": 131, "source_hdf5": "outputs/COLLECT_V1/runs/object_soft_pastry001_demo20012_sim_20260718_212723/replayed_demos/libero_object_task0_demo_20012.hdf5", "success": false, "task_id": 0, "task_suite": "libero_object"}
+{"demo_id": 20018, "language": "pick up the white swirled pastry and place it in the basket", "num_samples": 132, "source_hdf5": "outputs/COLLECT_V1/runs/object_soft_pastry001_demo20018_sim_20260718_213530/replayed_demos/libero_object_task0_demo_20018.hdf5", "success": false, "task_id": 0, "task_suite": "libero_object"}
+{"demo_id": 20019, "language": "pick up the white swirled pastry and place it in the basket", "num_samples": 127, "source_hdf5": "outputs/COLLECT_V1/runs/object_soft_pastry001_demo20019_sim_20260718_213622/replayed_demos/libero_object_task0_demo_20019.hdf5", "success": false, "task_id": 0, "task_suite": "libero_object"}
+{"demo_id": 20021, "language": "pick up the white swirled pastry and place it in the basket", "num_samples": 130, "source_hdf5": "outputs/COLLECT_V1/runs/object_soft_pastry001_demo20021_sim_20260718_213935/replayed_demos/libero_object_task0_demo_20021.hdf5", "success": false, "task_id": 0, "task_suite": "libero_object"}
+{"demo_id": 20023, "language": "pick up the white swirled pastry and place it in the basket", "num_samples": 128, "source_hdf5": "outputs/COLLECT_V1/runs/object_soft_pastry001_demo20023_sim_20260718_214112/replayed_demos/libero_object_task0_demo_20023.hdf5", "success": false, "task_id": 0, "task_suite": "libero_object"}
+{"demo_id": 20026, "language": "pick up the white swirled pastry and place it in the basket", "num_samples": 129, "source_hdf5": "outputs/COLLECT_V1/runs/object_soft_pastry001_demo20026_sim_20260718_214517/replayed_demos/libero_object_task0_demo_20026.hdf5", "success": false, "task_id": 0, "task_suite": "libero_object"}
+{"demo_id": 20027, "language": "pick up the white swirled pastry and place it in the basket", "num_samples": 128, "source_hdf5": "outputs/COLLECT_V1/runs/object_soft_pastry001_demo20027_sim_20260718_214606/replayed_demos/libero_object_task0_demo_20027.hdf5", "success": false, "task_id": 0, "task_suite": "libero_object"}
+{"demo_id": 20034, "language": "pick up the white swirled pastry and place it in the basket", "num_samples": 132, "source_hdf5": "outputs/COLLECT_V1/runs/object_soft_pastry001_demo20034_sim_20260718_215330/replayed_demos/libero_object_task0_demo_20034.hdf5", "success": false, "task_id": 0, "task_suite": "libero_object"}
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