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README.md

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 ---
 license: cc-by-4.0
+pretty_name: DexH13-VT
+size_categories:
+- n<1K
+task_categories:
+- robotics
+tags:
+- robotics
+- tactile
+- dexterous-manipulation
+- teleoperation
+- multimodal
+annotations_creators:
+- expert-generated
+- machine-generated
+source_datasets:
+- original
 ---
+
+# DexH13-VT
+
+DexH13-VT is a real-world vision–tactile teleoperation dataset of a right DexH13 hand. Each episode is one grasp of a household object, with joint positions, dense tactile forces, a palm camera, and an external RGB camera, all aligned at 30 Hz.
+
+The release contains **383 episodes**, **132,170 frames** (1.84 hours), and **49 objects**. Episodes are `ep_000000` through `ep_000382`, with no gaps.
+
+On the Hugging Face Hub the dataset is one archive, [`dexh13_vt.tar.zst`](https://huggingface.co/datasets/VatinZ/VT_Hand/blob/main/dexh13_vt.tar.zst), next to this card. Extract it, then the paths below match the unpacked `dexh13_vt/` directory:
+
+```bash
+tar --zstd -xf dexh13_vt.tar.zst
+```
+
+Inside the archive, each episode is NumPy arrays and JPEG frames. The Hub dataset viewer does not preview those files. Load them with the snippet below.
+
+## Summary
+
+| | |
+|---|---|
+| Episodes | 383 |
+| Frames | 132,170 |
+| Duration | 6,622 s (mean 17.3 s, range 9.9–48.3 s) |
+| Rate | 30 Hz |
+| Objects | 49 |
+| Hand | DexH13, right, 16 DoF |
+| Operator | one (`op_01`) |
+| Collected | 2026-08-12 to 2026-08-16 (UTC) |
+| Size on disk | about 15 GB |
+| Schema | `0.1.0` |
+
+### Tasks
+
+| `task_type` | Episodes |
+|---|---:|
+| `pick_place` | 140 |
+| `pick_hold` | 132 |
+| `deformable_grasp` | 99 |
+| `texture_grasp` | 12 |
+
+### Grasp labels
+
+| `grasp_type` | Episodes |
+|---|---:|
+| `pinch_2` | 237 |
+| `envelop` | 75 |
+| `tripod` | 65 |
+| `other` | 6 |
+
+Object attributes, counted by object rather than by episode: 18 boxes, 11 spheres, 10 cylinders, 10 irregular shapes; 22 hard, 10 medium, 17 soft. Size grades are near-equal bins of the grasp-relevant size `size_ref_mm`: XS 15.5–36 mm (13), S 38–48.3 mm (12), M 49–61 mm (12), L 62–99 mm (12).
+
+## Layout
+
+```text
+meta/dataset.json                 tactile layout and coordinate notes
+meta/object_labels.json           shape, hardness, and size for each object_id
+calibrations/                     camera intrinsics, hand–eye, and tactile baseline
+episodes/ep_XXXXXX/
+  meta.json                       episode record
+  label.json                      operator outcome and quality
+  events.json                     start, first contact, release, end
+  phases.json                     approach / contact / release / idle
+  timeline.npz                    per-frame index, time, and copies of the streams below
+  qpos/qpos.npy                   (T, 16) float32, measured joint position, radians
+  control/cmd_qpos.npy            (T, 16) float32, commanded joint position, radians
+  control/control_mode.npy        (T,) uint8
+  tactile/taxel_force.npy         (T, 1140, 3) float32, Newtons, sensor frame
+  tactile/tip_force.npy           (T, 11, 3) float32, Newtons, one vector per module
+  vision/palm/000000.jpg          640×480 JPEG, palm camera, one file per frame
+  vision/external/000000.jpg      640×480 JPEG, RealSense D435i color
+  vision/*_timestamps.npy         (T,) float64
+```
+
+`T` is `num_frames` in `meta.json` (179–692, mean 345). JPEG names are the frame index, zero-padded to 6 digits. In every episode, `timeline.npz` `palm_frame_idx` and `external_frame_idx` equal `frame_idx`, and both image folders contain exactly `num_frames` files. `timeline.npz` also stores `t_wall`, `t_mono`, `tip_force_norm` `(T, 11)`, `total_force` `(T,)`, `n_contact_modules` `(T,)`, and `qpos_clipped` `(T,)` bool.
+
+Joint order is the DexH13 SDK order. Values are radians.
+
+## Tactile layout
+
+`taxel_force[:, start:end, :]` is one sensing region. Order and slices are fixed and also written in `meta/dataset.json`. Stored values are Newtons (raw readings in units of 0.1 N, multiplied by 0.1), in the sensor-local xyz frame. `tip_force` follows the same module order.
+
+| Index | Module | Taxels | Slice |
+|---:|---|---:|---|
+| 0 | `index_base` | 120 | 0:120 |
+| 1 | `index_mid` | 60 | 120:180 |
+| 2 | `index_tip` | 120 | 180:300 |
+| 3 | `middle_base` | 120 | 300:420 |
+| 4 | `middle_mid` | 60 | 420:480 |
+| 5 | `middle_tip` | 120 | 480:600 |
+| 6 | `ring_base` | 120 | 600:720 |
+| 7 | `ring_mid` | 60 | 720:780 |
+| 8 | `ring_tip` | 120 | 780:900 |
+| 9 | `thumb_base` | 120 | 900:1020 |
+| 10 | `thumb_tip` | 120 | 1020:1140 |
+
+## Cameras
+
+Both streams are 640×480 color at 30 Hz. Depth is not recorded.
+
+| Camera | Device | Intrinsics |
+|---|---|---|
+| Palm | DexH13 palm camera | `calibrations/palm_camera/intrinsics_640x480.json` (fx ≈ 352.3, fy ≈ 352.8, cx ≈ 318.6, cy ≈ 219.9), plus distortion and the hand–eye matrix |
+| External | Intel RealSense D435i, color only | `calibrations/external_camera/realsense_640x480.json` (fx ≈ 607.6, fy ≈ 607.0, cx ≈ 320.2, cy ≈ 239.5) |
+
+## Labels
+
+`label.json` is filled by the operator at the end of the episode. Every episode has `outcome: success` and `object_feasible: true`. `quality_score` is an integer from 1 to 5:
+
+| Score | Episodes |
+|---:|---:|
+| 5 | 153 |
+| 4 | 176 |
+| 3 | 38 |
+| 2 | 3 |
+| 1 | 12 |
+| missing | 1 (`ep_000142`) |
+
+`events.json` and `phases.json` are produced by one automatic contact detector, with the same thresholds on every episode: a module counts as touching when its force exceeds 0.3 N, contact requires at least two modules and 0.8 N total, sustained for 100 ms, and the first 0.5 s of the episode is ignored. The primary phases are `approach`, `contact`, `release`, and `idle`.
+
+| Phase / event | Episodes |
+|---|---:|
+| `approach` | 383 |
+| `contact` / `first_contact` | 368 |
+| `release` | 72 |
+| `idle` (after release) | 72 |
+
+Fifteen episodes never cross the contact threshold, so they have no `contact` phase.
+
+`meta/object_labels.json` maps each `object_id` to `shape` (`sphere`, `cylinder`, `box`, `irregular`), `hardness` (`soft`, `medium`, `hard`), `size` (`XS`, `S`, `M`, `L`), `size_mm` (three measured extents), and `size_ref_mm` (one grasp-relevant scalar: diameter for a sphere; a diameter-like value for a cylinder; the short side for a box or irregular object, with a wrapped cross-section for very flat packs). `size_continuous` is `size_ref_mm / 99`. Forty-seven objects were measured with a ruler. `bread` and `bread_in_a_puffy_bag` use a vision-model estimate (`size_source: vlm_estimate`).
+
+## Objects
+
+Episode counts. Grasp and task columns list the labels present for that object.
+
+| `object_id` | Ep. | Shape | Hardness | Size | mm | Grasps | Tasks |
+|---|---:|---|---|---|---:|---|---|
+| `wooden_box` | 18 | box | hard | M | 50.0 | envelop, pinch_2, tripod | pick_hold, pick_place |
+| `climbing_hold` | 15 | irregular | hard | XS | 33.0 | envelop, pinch_2, tripod | pick_hold, pick_place |
+| `moon_ball` | 15 | sphere | medium | L | 76.0 | envelop, pinch_2, tripod | pick_hold, pick_place |
+| `tennis` | 13 | sphere | hard | M | 61.0 | pinch_2, tripod | pick_hold, pick_place |
+| `bottle` | 12 | cylinder | medium | L | 96.0 | envelop, pinch_2, tripod | deformable_grasp, pick_place |
+| `umbrella` | 12 | cylinder | medium | S | 43.0 | envelop, other, pinch_2 | pick_hold, pick_place |
+| `vertical_striped_paper_tube` | 12 | cylinder | medium | M | 54.0 | other, pinch_2 | pick_hold, pick_place |
+| `weighted_bubble_roll` | 12 | cylinder | soft | L | 62.0 | envelop, pinch_2, tripod | deformable_grasp, pick_place |
+| `sponge_block` | 11 | box | soft | L | 99.0 | envelop, pinch_2, tripod | deformable_grasp, pick_place |
+| `sandbag` | 10 | box | soft | L | 64.0 | pinch_2, tripod | deformable_grasp, pick_place |
+| `baseball` | 9 | sphere | hard | L | 69.0 | pinch_2, tripod | pick_hold, pick_place |
+| `bouncy_ball` | 9 | sphere | soft | L | 64.0 | pinch_2, tripod | pick_hold, pick_place |
+| `bubble_wrap_roll` | 9 | cylinder | soft | M | 49.0 | envelop, pinch_2, tripod | deformable_grasp, pick_place |
+| `flower_foam` | 9 | box | medium | L | 65.0 | pinch_2, tripod | deformable_grasp, pick_place |
+| `hard_massage_ball` | 9 | sphere | hard | L | 64.0 | envelop, pinch_2 | pick_place, texture_grasp |
+| `iron_ball` | 9 | sphere | hard | M | 55.0 | pinch_2, tripod | pick_hold, pick_place |
+| `metal_box` | 9 | box | hard | XS | 31.1 | pinch_2, tripod | pick_hold, pick_place |
+| `paper_tube` | 9 | cylinder | medium | S | 48.3 | pinch_2, tripod | pick_hold, pick_place |
+| `pillow` | 9 | box | soft | XS | 36.0 | pinch_2, tripod | deformable_grasp, pick_place |
+| `rockery_model` | 9 | irregular | hard | S | 48.0 | envelop, pinch_2 | pick_hold, pick_place |
+| `water_filled_bag` | 9 | irregular | soft | S | 47.6 | envelop, pinch_2, tripod | deformable_grasp |
+| `wooden_ball` | 9 | sphere | hard | L | 63.0 | pinch_2, tripod | pick_hold, pick_place |
+| `stiff_slime` | 7 | irregular | medium | M | 50.0 | pinch_2 | deformable_grasp, pick_place |
+| `checkered_cylinder` | 6 | cylinder | hard | M | 52.0 | envelop | pick_place, texture_grasp |
+| `green_dragon` | 6 | box | hard | XS | 25.0 | pinch_2 | pick_hold, pick_place |
+| `massage_stick_with_dots` | 6 | cylinder | hard | L | 70.0 | envelop | pick_hold, pick_place |
+| `molecular_model` | 6 | irregular | medium | M | 53.0 | pinch_2 | pick_hold, pick_place |
+| `one_of_circles` | 6 | box | hard | XS | 25.0 | pinch_2 | pick_hold, pick_place |
+| `perforated_plastic_block` | 6 | box | hard | M | 50.0 | pinch_2 | pick_hold, pick_place |
+| `pingpong_ball` | 6 | sphere | hard | S | 38.0 | pinch_2 | pick_hold, pick_place |
+| `plastic_box` | 6 | box | hard | XS | 22.0 | pinch_2 | pick_hold, pick_place |
+| `red_dragon` | 6 | box | hard | XS | 25.0 | pinch_2 | pick_hold, pick_place |
+| `resin_block` | 6 | box | hard | S | 39.0 | pinch_2 | pick_hold, pick_place |
+| `rubber_ball` | 6 | sphere | medium | S | 48.0 | pinch_2 | deformable_grasp, pick_place |
+| `soft_ball` | 6 | sphere | soft | L | 63.0 | pinch_2 | deformable_grasp, pick_place |
+| `soft_slime` | 6 | irregular | soft | XS | 30.9 | envelop, pinch_2 | deformable_grasp |
+| `spiky_massage_stick` | 6 | cylinder | hard | M | 54.0 | envelop | pick_hold, pick_place |
+| `striped_cylinder` | 6 | cylinder | hard | M | 52.0 | envelop | pick_place, texture_grasp |
+| `white_dragon` | 6 | box | hard | XS | 25.0 | pinch_2 | pick_hold, pick_place |
+| `wooden_block` | 6 | box | hard | M | 60.0 | pinch_2, tripod | pick_hold |
+| `yarn_ball` | 6 | sphere | soft | S | 45.0 | pinch_2 | deformable_grasp, pick_place |
+| `plasticine` | 5 | irregular | soft | XS | 28.0 | pinch_2 | deformable_grasp, pick_place |
+| `silicone_toy` | 5 | irregular | soft | S | 48.0 | pinch_2 | deformable_grasp, pick_place |
+| `small_doll` | 5 | irregular | soft | XS | 31.0 | pinch_2 | deformable_grasp, pick_place |
+| `bread` | 3 | box | soft | S | 40.0 | pinch_2 | deformable_grasp |
+| `bread_in_a_puffy_bag` | 3 | box | soft | S | 42.0 | pinch_2 | deformable_grasp |
+| `fluff_filled_resealable_bag` | 3 | box | soft | XS | 29.8 | pinch_2 | deformable_grasp |
+| `ice_strip` | 3 | irregular | soft | XS | 15.5 | pinch_2 | deformable_grasp |
+| `take_out_box` | 3 | box | medium | S | 41.0 | pinch_2 | deformable_grasp |
+
+## Loading
+
+```python
+import json
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+root = Path("dexh13_vt")  # or the directory from snapshot_download
+episode = root / "episodes" / "ep_000001"
+meta = json.loads((episode / "meta.json").read_text())
+labels = json.loads((root / "meta" / "object_labels.json").read_text())
+
+qpos = np.load(episode / "qpos" / "qpos.npy")             # (T, 16)
+taxel = np.load(episode / "tactile" / "taxel_force.npy")  # (T, 1140, 3)
+tip = np.load(episode / "tactile" / "tip_force.npy")      # (T, 11, 3)
+frame = Image.open(episode / "vision" / "palm" / "000000.jpg")
+obj = labels[meta["object_id"]]
+```
+
+```python
+from huggingface_hub import snapshot_download
+
+path = snapshot_download("YOUR_ORG/dexh13-vt", repo_type="dataset")
+```
+
+There is no official train/test split. To test generalization to new objects, hold out entire `object_id` values rather than random episodes of the same object.
+
+## Collection
+
+An operator teleoperated the hand, then switched to a hold with a manual key (`control_policy.mode_primary` is `teleop_then_hold`, `hold_trigger` is `manual_G`). Human hand motion was retargeted onto the DexH13 (checkpoint tag `modellatest`) with rectification, wrist recentering, an EMA of 0.35, and thumb equalization. `apply_pinch_remap` is false on every episode. `control/control_mode.npy` is present and is 0 on every frame; the policy is recorded in `meta.json`, not in that array.
+
+`qpos_clipped` in `timeline.npz` is true on 15,998 frames (12.1%), meaning at least one joint was at its limit on that frame.
+
+## Considerations
+
+- One operator and one right hand. Results do not cover left hands or other people.
+- Every episode is labeled a success. Use `quality_score` to separate clean trials from poor ones. Twelve episodes are scored 1, and `ep_000142` has no score.
+- Contact and release labels are automatic. Fifteen episodes have no detected contact, and a release is marked on only 72 episodes.
+- The RealSense recording is color only.
+- Palm and external images show the operator's hand and the lab bench. `operator_id` is a pseudonym (`op_01`).
+- Joint names are not stored. Use the DexH13 SDK joint order.
+- `bread` and `bread_in_a_puffy_bag` sizes are vision-model estimates, not ruler measurements.
+
+## License
+
+[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) (`cc-by-4.0`). You may share and adapt the dataset, including for commercial use, if you give appropriate credit.
+
+## Citation
+
+Cite this dataset repository when you use the data. A paper citation will be added here when it is available.

dexh13_vt.tar.zst

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