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, next to this card. Extract it, then the paths below match the unpacked dexh13_vt/ directory:
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
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
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"]]
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_idis a pseudonym (op_01). - Joint names are not stored. Use the DexH13 SDK joint order.
breadandbread_in_a_puffy_bagsizes are vision-model estimates, not ruler measurements.
License
Creative Commons Attribution 4.0 International (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.