Sourcing tactile glove data

Quick facts

STAG (MIT, 2019)

Scalable Tactile Glove — 548-sensor knit glove dataset of grasps; published in Nature 569:698-702.

ReSkin (Meta AI, 2021)

Magnetic skin sensor dataset and platform — replaceable, tactile-rich substrate for contact-rich learning.

TVL (CoRL 2023)

Touch-Vision-Language dataset pairs visual frames with synchronized DIGIT tactile sensor readings.

Why custom capture

Glove sensor layouts (resistive, capacitive, magnetic) and sample rates differ; off-the-shelf data rarely matches the buyer's target end-effector.

Spec checklist

Sensor layout schematic, calibration matrix, sample rate, sync offset to video, contact-phase annotations, object material/class taxonomy.

Comparison

Dataset requirements

Buyers should specify per-finger force or tactile signals with synchronized video, accepted scenes in grasping, tool use, insertion, fabric, and deformable object tasks, a minimum of 10 hours of useful captured volume, consent rules, and the exact metadata package: sensor layout, calibration, contact phase, object class, and task outcome ^[1]. The Datasheets framework spells out which dataset-documentation questions matter before any commercial training program begins.

"The machine learning community currently has no standardized process for documenting datasets, which can lead to severe consequences in high-stakes domains."

Best-fit buyers

The strongest fit is teams working on contact-rich manipulation ^[3]. It can also work as a smaller eval set — typically 100 to 500 episodes — before a larger net-new capture program.

Tactile glove data sample package

A credible tactile glove data supplier should provide a sample package that includes raw files, a manifest, capture context, and these critical metadata fields: sensor layout, calibration, contact phase, object class, and task outcome ^[4]. Across at least 10 representative episodes, the buyer should be able to inspect whether per-finger force or tactile signals with synchronized video actually appears in grasping, tool use, insertion, fabric, and deformable object tasks, not just trust a verbal description of the inventory.

Tactile glove data licensing check

The licensing review for tactile glove dataset should confirm whether the data is off-the-shelf or net-new, whether it can be used for commercial model training, whether contributors or sites consented, and whether the supplier can reproduce the same rights package for the full delivery ^[5]. A well-scoped buyer typically reviews 3 to 5 supplier deliveries before approving scale; without those checks, an apparently useful dataset can become a legal or procurement blocker.

Related pages

Use these to move from category-level context into specific task, dataset, format, and comparison detail.

External references and source context

1. Datasheets for Datasets

   Supports the dataset-requirements framework dimensions: dataset motivation, composition, collection process, recommended uses, and license review.

   arXiv ↩
2. Datasheets for Datasets

   Datasheets for Datasets defines the consent, provenance, and intended-use questions buyers must ask before commercial training; quoted verbatim in the dataset-requirements section.

   arXiv ↩
3. Open X-Embodiment: Robotic Learning Datasets and RT-X Models

   Open X-Embodiment establishes the cross-embodiment robotics pretraining baseline — a useful reference for buyer fit when mapping a deployment-specific dataset onto a generalist policy.

   arXiv ↩
4. Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI

   Data Cards capture dataset origins, development, intent, and ethical considerations buyers can attach to each delivered batch for procurement audit.

   arXiv ↩
5. encord

   Commercial vendors deliver licensed dataset collection programs with explicit contributor consent, rights, and per-batch documentation buyers can audit before scale.

   encord.com ↩

FAQ