— Use Case · Failure Case Capture
Capture the moments that break deployment.
Models fail on the data they never saw: occlusions, slips, clutter, bad lighting, ambiguous states. dexset captures failure cases deliberately — scripted, labeled, and structured — so the rarest scenarios stop being surprises.
Failure modes · Slip, occlusion, lighting
— Capabilities
How failure capture works.
Deliberate failure scripting
Failure modes reproduced on purpose: mis-grips, drops, occlusions, and collisions staged safely and repeatedly.
Failure taxonomies
A structured label system for failure type, cause, severity, and recovery — not just a generic “failed” tag.
Edge-condition capture
Low light, glare, reflective surfaces, dense clutter, and degraded environments captured systematically.
Intervention logging
Teleoperation takeovers and human corrections recorded as paired failure-plus-recovery examples.
Near-miss capture
The ambiguous, almost-failed attempts that sit on the decision boundary — often the most valuable frames.
Failure evaluation sets
Held-out failure benchmarks so you can measure robustness, not just average-case accuracy.
— In Scope
Failure modes in scope.
— 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
★★★★★
“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
★★★★★
“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.
Why capture failures on purpose?
Because models fail on what they never saw, and natural failure footage is rare. Scripted failures — mis-grips, drops, occlusions, bad lighting — produce dense, labeled coverage of exactly the conditions that break deployment.
How are failures labeled?
Against a structured taxonomy: failure type, cause, severity, and recovery, plus near-miss tags for attempts on the decision boundary. A generic “failed” flag is never the final label.
Can you reproduce the failures we see in deployment?
Yes. Send us your failure reports or intervention logs and we script reproductions across controlled variation, turning one observed incident into systematic training coverage.
Do you build failure-focused evaluation sets?
Yes. Held-out failure benchmarks, captured independently of training data, let you measure robustness directly instead of inferring it from average-case accuracy.
What breaks your model today?
Send us your failure reports. We turn them into a capture plan.