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Dexset

— Teleoperations

Teleoperation data for continuous robot learning.

dexset supports teleoperation workflows that help robotics teams collect operator data, capture deployment edge cases, and improve models after real-world rollout.

— The Data Loop

Every intervention is a training signal.

Teleoperation is not only an operational backup. It is a data loop. Each time an operator takes over, that moment captures exactly where the model fell short — and what the correct behavior looks like.

Teleoperation session with interventions

Session TEL_2024 · 24 interventions · 23 new edge cases

Remote & on-site operation

Support for remote piloting and in-environment operation, with synchronized capture of operator inputs and outcomes.

Edge-case capture

Flag, store, and structure intervention moments so the rarest scenarios become your most valuable training data.

Human-in-the-loop learning

Feed operator corrections back into fine-tuning sets to close the gap between this model version and the next.

— 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.

GDPRUK GDPRCCPA / CPRAPIPLAPPILGPDData residency options: EU · US · APAC

— What Teams Say

Trusted by robotics teams.

★★★★★

“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

★★★★★

“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

★★★★★

“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

— FAQ

Frequently asked questions.

Why does teleoperation matter for training data?

Every operator intervention marks a moment the model failed and shows the correct behavior in the same context. Captured and structured, these interventions become paired failure-and-recovery examples — the highest-value fine-tuning data a deployed system can generate.

Yes. We support remote piloting setups and in-environment operation, with synchronized recording of operator inputs, robot state, and camera views so each session is fully reconstructable as training data.

State-action pairs, intervention events, new edge cases, multi-view video, and session metadata — operator, timing, and environment. The teleoperation dashboard tracks interventions and newly discovered edge cases per session.

Intervention clips enter the same annotation and quality pipeline as planned captures. For active deployments, structured intervention batches can be delivered on a weekly cadence so each fine-tuning round includes the latest edge cases.

Turn your deployment into a dataset.