— Dataset Annotation
Turn raw robotics footage into structured training data.
dexset annotates videos, frames, objects, actions, poses, task states, failures, and outcomes so robotics teams can train and evaluate models faster.
— Annotation Types
Labels that understand physical tasks.
Bounding boxes
Segmentation masks
Object labels
Action labels
Pose / keypoint labels
Hand-object interaction
Task state labels
Failure event labels
Environment metadata
Temporal sequence labels
— Annotated Output
What labeled frames look like.
Task-state + object labels · Industrial assembly
Multi-view + placement validation · Tool organization
— Quality & Delivery
Reviewed by humans. Delivered in your format.
Quality assurance
Multi-pass review, annotation confidence scoring, inter-annotator agreement checks, and error flagging before anything ships.
Human review workflows
Robotics-aware reviewers validate task states, grasp points, and failure markers with the physical context generic vendors miss.
Export formats
COCO, YOLO, Pascal VOC, JSON, CSV, or a custom schema mapped to your training pipeline.
— Compliance & Trust
Compliant by design, across every dataset.
From first consent form to final delivery, dexset workflows are built to meet data protection, security, and labor standards across the regions where we capture and the regions where our customers operate.
Informed consent
Every participant is briefed and signs a release before capture begins. Consent records attach to each clip, and withdrawal requests are honored across all delivered versions.
Privacy protection
Face blurring, anonymization, and exclusion zones are applied wherever required. We minimize personal data by design and never collect more than the task brief demands.
Data security
Footage is encrypted in transit and at rest, with role-based access controls, signed delivery URLs, and per-recipient transfer records on every dataset.
Regional compliance
Capture and delivery workflows are designed to align with GDPR and UK GDPR, CCPA/CPRA, PIPL, APPI, and LGPD requirements, with regional data-residency options where needed.
Traceability & audit
Source, consent, capture, environment, and annotation records persist for every dataset, supporting audits that run from raw footage to the exported training file.
Responsible sourcing
Capture operators, demonstrators, and annotators are fairly paid and work under documented, safe conditions — quality data should never come from exploitative labor.
— What Teams Say
Trusted by robotics teams.
★★★★★
“The egocentric hand-pose labels are the best we have evaluated. Our grasp success rate improved 18% after one fine-tuning round.”
Priya Raman
Manipulation Lead, AI Robotics Company
★★★★★
“From brief to delivered COCO export in three weeks. Generic annotation vendors quoted us three months for lower quality.”
David Park
Founding Engineer, Independent Robotics Vendor
★★★★★
“dexset captured 400 hours of bin-picking demonstrations across three warehouses. The failure taxonomy alone cut our triage time in half.”
Lena Ortiz
Head of Robot Learning, AI Robotics Company
— FAQ
Frequently asked questions.
What annotation types do you support?
Bounding boxes, instance segmentation masks, object and action labels, 21-point hand pose, hand-object interaction labels, task-state labels, failure-event markers, temporal sequence segmentation, and environment metadata — applied per frame, per clip, or per task as your spec requires.
How do you assure annotation quality?
Multi-pass review with inter-annotator agreement checks, confidence scoring on every label, and robotics-aware reviewers who validate grasp points and task states in physical context. Batches ship only after error flags are resolved and agreed thresholds are met.
Can you annotate footage we already have?
Yes. We annotate customer-supplied footage to the same standards as our own captures. We first run an intake review to confirm the footage can support the labels you need — and flag gaps a targeted capture could fill.
What formats can annotations be delivered in?
COCO, YOLO, Pascal VOC, JSON, and CSV as standard, or a custom schema mapped to your internal taxonomy. Schema mapping is defined once at project start and applied to every delivery automatically.
Send us a sample clip.
We will return it annotated to your spec so you can judge the quality directly.