— Use Cases
Robotics data for every stage of model development.
From pre-training corpora to post-deployment failure capture, dexset fits wherever your ML workflow needs real-world ground truth.
Pre-training data collection
Models need broad coverage before they specialize.
Large-scale, diverse task demonstrations across environments and operators.
Fine-tuning datasets
Generic models miss your specific objects and workflows.
Narrow, high-precision sets built around your exact task brief.
Model evaluation sets
You cannot trust metrics computed on training-adjacent data.
Held-out, independently captured benchmarks with traceable provenance.
Failure case capture
The cases that break deployment rarely exist in your data.
Deliberate capture of occlusions, slips, clutter, and ambiguous states.
Teleoperation feedback loops
Operator interventions are discarded instead of learned from.
Structured intervention data ready for the next fine-tuning round.
Sim-to-real validation
Simulation results do not survive contact with reality.
Matched real-world sets to validate and calibrate simulated training.
Perception model training
Detection and segmentation need labeled physical context.
Densely annotated frames with objects, masks, and scene metadata.
Manipulation model training
Grasping policies need examples of contact-rich interaction.
Hand pose, grasp point, and outcome labels across object variation.
— 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.
★★★★★
“Coverage scoring meant we knew exactly what variation we were missing before training, not after burning a GPU budget on it.”
Sofia Almeida
Perception Engineer, AI Robotics Company
★★★★★
“Their teleoperation intervention capture turned our deployment into a data source. Every takeover now feeds the next training run.”
Jonas Weber
CTO, Independent Robotics Vendor
★★★★★
“We sent one task brief and got back a dataset that loaded into our pipeline on the first try. Custom schema, zero rework.”
Marcus Chen
ML Infrastructure Lead, Independent Robotics Vendor
— FAQ
Frequently asked questions.
Where in the ML lifecycle does dexset fit?
Across all of it: broad demonstration corpora for pre-training, narrow high-precision sets for fine-tuning, independently captured held-out benchmarks for evaluation, deliberate failure-case capture for robustness, and teleoperation feedback loops for post-deployment improvement.
Can you build evaluation sets independent of our training data?
Yes — and you should. Evaluation sets are captured in separate sessions, with separate operators and environments where required, and documented provenance, so your metrics reflect generalization rather than training-set leakage.
How do you decide how much data a use case needs?
We scope volume against your training objective: task complexity, variation axes, and the failure modes you need covered. Pilots establish a baseline; coverage scoring then shows which variation is missing before you commit to scale.
Do you support continuous data programs?
Yes. Many teams run recurring monthly capture and annotation batches tied to deployment feedback, so each model release trains on the newest edge cases instead of a frozen snapshot.