Datasets:

VatinZ
/

VT_Hand

---
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
```


## 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
  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`.

`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.
- 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.

	---
	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
	```


	## 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
	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`.

	`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.
	- 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.