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float32
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240
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int64
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3.3
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End of preview. Expand in Data Studio

Language: English (current) · 中文

Xense Robotics

Representative frames from TacVerse's bimanual demonstrations.

TacVerse collection and processing pipeline

Collected with XTac-UMI-G1 grippers, released as LeRobot datasets.

TacVerse Open Data

Collection of 122 LeRobot v3.0 task datasets — 17,690 episodes, 370.2 hours, 40.0M frames, ~145 GB. Each subfolder is a standalone LeRobot dataset (meta/info.json, data/, videos/). Collection timestamps have been removed from titles and metadata.

Every frame carries six synchronized video streams — two wrist cameras and four Xense Neutron visuotactile channels (two per gripper) — at 30 fps, alongside per-hand 6-DoF pose and continuous jaw aperture.

from huggingface_hub import snapshot_download
from lerobot.datasets.lerobot_dataset import LeRobotDataset

root = snapshot_download("TacVerse/opendata", repo_type="dataset")
ds = LeRobotDataset("taccap-g1-wipe-mirror", root=f"{root}/taccap-g1-wipe-mirror")

At a glance

This collection Planned total
Task datasets 122 350+
Scene families 3 3
Episodes 17,690 ~480,000
Hours 370.2 10,000+
Frames 39,977,972 ~1.1B
Frame rate 30 fps 30 fps
Visual streams 2 wrist (640×480)
Tactile streams 4 (700×400)
Robot type bi_taccap_gripper
Format LeRobot v3.0 LeRobot v3.0
Total size ~145 GB ~13 TB
Size per hour 0.39 GB

This collection is measured from this repository. Planned total is the TacVerse programme's target — this release is one openly published slice of it; see the landing page for programme-wide figures. Planned episodes, frames and size are all extrapolated from this repository's own density (48 episodes and 0.39 GB per hour); the planned size follows the programme's own 1.3 GB per hour, not this repository's re-encoded density.

Scenes

The collection spans three scene families.

Scene families and representative objects

Scene Datasets Episodes Hours
Office 44 3,028 55.5
Home 70 13,124 286.9
Workbench 8 1,538 27.7
Total 122 17,690 370.2

Task families

Scene Task family Datasets Episodes Hours Largest tasks
Office Desk tidying 5 138 2.7 arrange-desk-items, remove-item-from-display-cover, put-item-in-display-cover
Document handling 3 51 1.5 flip-bound-document, pull-single-sheet, organize-folders
Stationery 2 31 1.5 paper-punching, arrange-pens-sticky-notes
Print area 3 231 9.3 organize-printing-paper-stack, organize-printing-papers-stack, remove-and-place-paper
Meeting room 4 65 2.6 organize-meeting-table-items, prepare-whiteboard-marker, align-remote-and-laser-pointer
Pantry 7 194 3.9 tear-open-the-coffee-bag, scoop-coffee-granules, grasp-the-water-cup-and-twist-off-the-cup-lid
Cable management 12 1,824 28.7 plug-the-charger-into-the-power-strip, connect-the-charger-to-the-type-c-data-cable, locate-cover-grip-clip-into-slot
Connectors and ports 8 494 5.2 unplug-usb-cable-from-port, insert-usb-connector-into-port, put-earphone-into-case
Home Kitchen and dining 15 1,256 39.3 place-food-with-tongs, rotate-seasoning-bottle-to-target, place-egg-into-egg-tray
Drinks and food service 6 329 8.5 place-paper-cup, pick-up-bread-slices, assemble-and-place-cup
Food packaging 10 925 13.1 open-resealable-bag, arrange-food-packaging-bags, pick-up-marshmallows
Household items 13 2,778 51.9 arrange-tabletop-items, press-remote-buttons, place-chess-pieces
Cabinets and storage 6 3,267 66.2 operate-shoe-box, snap-plastic-buckle, insert-hook-assembly
Cleaning 4 1,131 16.8 wipe-mirror, clean-desk, mask-extraction-and-stacking
Clothing and textiles 5 2,389 54.6 fold-garment, open-folded-towel, fold-towel
Personal care 7 996 34.5 arrange-skincare-products, cosmetics-are-scanned-and-boxed, press-pump-bottle
Medical and lab 4 53 2.0 liquid-medicine-bottles-cartoning, arrange-medicine-boxes, identify-and-place-reagent-bottle
Workbench Precision assembly 3 1,061 19.2 place-washer-onto-stud, insert-divider, insert-jst-connector
Inspection and sorting 5 477 8.6 compare-elastomer-hardness, sort-resistors, sort-screws-nuts-washers-into-bins

Sensor key map

Sensor key map — each image stream keyed to its physical mount

Tactile keys map to fingertip sensors; wrist keys to gripper cameras.

This collection carries the two wrist and four tactile keys only; it has no head streams.

Provenance

These datasets were assembled from the TacVerse capture programme and re-exported for this release. They do not carry meta/sessions.json, so the per-day capture sessions behind each task are not recoverable from the files themselves.

End-effector poses were recomputed where the capture stack had not yet applied the measured tracker→TCP mount transform (added upstream on 2026-08-01). Only the 9 pose dimensions per arm were touched — jaw aperture, wrist video and tactile video are the captured values throughout.

License

Citation

BibTeX:

@misc{tacverse2026,
  title        = {TacVerse: A Large-Scale Bimanual Visuo-Tactile Manipulation Dataset},
  author       = {Xense Robotics},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/TacVerse/TacVerse}},
  note         = {Collected with the XTac-UMI-G1 handheld gripper and Xense Neutron visuotactile sensors}
}
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