Hugging Face's logo

Datasets:
Arthur12137
/
SoftVTBench

Tasks:
Robotics
Modalities:
Video
Size:
10K - 100K
Tags:
manipulation
tactile
visuo-tactile
deformable-objects
soft-body
libero
Libraries:
Datasets
License:
Dataset card Data Studio Files
xet
 Community
1
SoftVTBench / README.md
Arthur12137's picture
Arthur12137
Add files using upload-large-folder tool
efabe88 verified
preview code
|
Raw
History Blame Contribute Delete
5.62 kB
metadata 
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:

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 

pip install -U huggingface_hub
hf download Arthur12137/SoftVTBench --repo-type dataset --local-dir ./SoftVTBench

pip install -U modelscope
modelscope download --dataset Arthur12137/SoftVTBench --local_dir ./SoftVTBench

License

Apache-2.0.