| --- |
| license: apache-2.0 |
| task_categories: |
| - robotics |
| tags: |
| - manipulation |
| - tactile |
| - visuo-tactile |
| - deformable-objects |
| - soft-body |
| - libero |
| - isaac-lab |
| pretty_name: SoftVTBench |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # SoftVTBench |
|
|
| 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) |
|
|
| Earlier releases (evaluation USD assets and the first partial data drops) were |
| moved to `Arthur12137/SoftVTBench-archive` on both hubs. |
|
|
| ## Contents |
|
|
| Four subsets, each **10 tasks × 100 successful demonstrations = 1000 demos** |
| and **4000 videos**. |
|
|
| | 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 | |
|
|
| Every episode carries `success = True`; failures were filtered out at assembly |
| time (the skipped records are kept in `failure.jsonl`). |
|
|
| ## Layout |
|
|
| ``` |
| <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 |
| ``` |
|
|
| ## 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) |
| ``` |
|
|
| `obs/` fields, all four subsets: |
|
|
| `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` |
|
|
| The two soft subsets add four deformation channels: |
| `fem_deformation_max`, `fem_deformation_rms`, |
| `fem_kabsch_max_pct`, `fem_kabsch_rms_pct`. |
|
|
| `gripper_marker_motion` is the tactile signal: `(T, 2, 2, 99, 2)` — two GelSight |
| Mini pads × (reference, current) × 99 markers × (x, y) displacement. |
|
|
| 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. |
|
|
| ## Verified integrity |
|
|
| Checked over the full release, not sampled unless noted: |
|
|
| - 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 |
|
|
| - **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 |
| |
| ```bash |
| pip install -U huggingface_hub |
| hf download Arthur12137/SoftVTBench --repo-type dataset --local-dir ./SoftVTBench |
| ``` |
| |
| ```bash |
| pip install -U modelscope |
| modelscope download --dataset Arthur12137/SoftVTBench --local_dir ./SoftVTBench |
| ``` |
| |
| ## License |
| |
| Apache-2.0. |
| |
