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Field notes for robotics data teams

Practical notes on human demonstrations, capture programs, consent, anonymization, annotation schemas, and dataset QA.

Human demonstrations, metadata schemas, consent, QA

Data Strategy / 8 min read

How to build a robotics training dataset that teams can trust

A practical operating model for planning human demonstrations, camera coverage, annotations, consent, QA, and delivery before collection scales.

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Field notes for better decisions

Data Strategy / 2026-09-09

How to build a robotics training dataset that teams can trust

Use this five-part framework to plan a robotics training dataset that is useful to researchers, engineers, legal teams, and procurement.

VLA Data / 2026-09-08

What VLA training data needs from real human actions

Learn which human action, object, camera, and task fields make real-world video more useful for vision-language-action model development.

Evaluation / 2026-09-07

Physical AI dataset evaluation guide for engineering teams

A concise review checklist for comparing physical AI datasets before a pilot or enterprise procurement decision.

Data Specs / 2026-06-06

Human demonstration dataset checklist for robotics teams

Learn which fields matter when evaluating human action video for robotics: task labels, hands, objects, camera view, quality score, and consent status.

Data Strategy / 2026-06-06

Egocentric vs exocentric data: which does your robot need?

Compare egocentric and exocentric robotics datasets, including their strengths, limitations, capture requirements, and best-fit use cases.

Data Specs / 2026-06-06

Metadata schemas for robot learning datasets

Learn how to structure robotics dataset metadata across tasks, actions, objects, hands, cameras, timestamps, quality, consent, and annotations.

Data Strategy / 2026-06-06

From simulation to real: robotics data practices that improve transfer

Explore practical sim-to-real robotics data strategies, including domain coverage, real-world calibration, co-training, and evaluation.

Capture Ops / 2026-06-06

Bimanual manipulation video: what makes the data useful

Learn how to collect and evaluate bimanual manipulation video datasets for folding, assembly, food preparation, and tool-use tasks.

Industry / 2026-06-06

Human demonstration data for industrial robotics workflows

A guide to collecting industrial robotics datasets across assembly, inspection, garment work, material handling, and tool-use workflows.

Compliance / 2026-06-05

How to evaluate commercial-use rights for human robotics data

A concise guide to consent-first robotics datasets, anonymization, and commercial-use data collection.

Capture Ops / 2026-06-03

Egocentric vs exocentric data for embodied AI

Explore capture quality checks for egocentric and exocentric human task videos used in robotics training.

Environments / 2026-06-02

Why Indian household data matters for physical AI

Learn how Indian household task data can broaden the environments and manipulation patterns represented in physical AI datasets.

Manipulation / 2026-06-01

Collecting bimanual and deformable-object manipulation data

A practical guide to capturing and annotating bimanual manipulation data for robotics training.

Data Specs / 2026-05-30

A practical metadata schema for robot learning datasets

See the core clip, task, camera, quality, consent, and annotation fields for a robot learning dataset.

Built for teams that need inspectable real-world robotics data, clear consent workflows, and practical capture operations.

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