| --- |
| license: apache-2.0 |
| task_categories: |
| - robotics |
| tags: |
| - manipulation |
| - tactile |
| - deformable-objects |
| - soft-body |
| - libero |
| - isaac-lab |
| 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). |
|
|
| 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 × 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. |
|
|
| `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. |
|
|
| ## Layout |
|
|
| ```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 |
| ``` |
|
|
| Inside each HDF5, `data/demo_N` contains: |
|
|
| - `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 |
|
|
| 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-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. |
|
|
| 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. |
|
|
| If you use this dataset, please cite LIBERO and Tabero alongside SoftVTBench. |
|
|
