Sourcing spec
Sourcing tactile glove data
A tactile glove dataset is useful for teams working on contact-rich manipulation. When sourcing it, specify per-finger force or tactile signals with synchronized video, capture in grasping, tool use, insertion, fabric, and deformable object tasks, and sensor layout, calibration, contact phase, object class, and task outcome so supplier samples can be reviewed before adoption.
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
| Option | Strength | Gap |
|---|---|---|
| Generic dataset | Fast discovery | Usually lacks the buyer's rights and metadata |
| Public benchmark | Academic baseline | Often not fit for commercial deployment |
| truelabel sourcing | Spec-matched supplier samples | Needs buyer review before scale-up |
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, consent rules, and a pilot package large enough to include accepted clips, rejected clips, metadata, consent/provenance artifacts, and edge cases for buyer review. The exact metadata package should include: 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.
[2]"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 before a larger net-new capture program when the buyer defines the held-out tasks, failure cases, and acceptance rubric in writing.
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]. The buyer should be able to inspect accepted and rejected examples to confirm 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 reviews multiple supplier samples 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
- Datasheets for Datasets
Supports the dataset-requirements framework dimensions: dataset motivation, composition, collection process, recommended uses, and license review.
arXiv ↩ - 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 ↩ - 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 ↩ - 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 ↩ - 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
What is a tactile glove dataset?
It is a dataset focused on grasping, tool use, insertion, fabric, and deformable object tasks using per-finger force or tactile signals with synchronized video. The buyer should require sensor layout, calibration, contact phase, object class, and task outcome for provenance and training-readiness.
Can this be off-the-shelf?
Yes. Suppliers can respond with existing datasets if they can prove rights, consent, and metadata coverage for the buyer's spec.
What makes the dataset usable for training?
The dataset needs consistent files, task labels, timestamps or clip boundaries, rights, consent artifacts, and a delivery manifest that matches the buyer's pipeline.
How does truelabel route this request?
truelabel routes the request to suppliers whose capability profile matches the requested modality, environment, geography, rights, and delivery format.
Looking for tactile glove dataset?
Specify modality, task, environment, requested rights posture, and delivery format. Truelabel routes the request to candidate capture partners and helps scope consent/provenance artifacts and commercial licensing requirements for buyer review before delivery.
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