How to source egocentric video for robotics

Editorial guidance by Robotics Data Collection · Research method

Evaluate viewpoint, task coverage, annotations and rights before treating first-person video as useful robot training data.

First-person video can show human activity from a useful perspective. It does not automatically provide the synchronized robot actions required by your learning setup. Decide which part of your training pipeline the recording is meant to support before comparing suppliers.

Match viewpoint to the learning objective

A head-mounted camera captures scene context but can lose sight of hands during a task. Wrist views can make local interaction easier to inspect while revealing less of the environment. Ask to see the actual recording configuration and an unedited task sequence.

Define when both hands, the manipulated object and the relevant workspace must be visible. Specify occlusion tolerance and whether blurred or interrupted segments count toward purchased volume. Request examples of challenging conditions as well as successful demonstrations.

Separate video from additional signals

Ask for an explicit inventory: RGB, depth, pose, robot or gripper state, action labels, language instructions and calibration. Establish which signals were measured, inferred or annotated after capture. These distinctions affect how you can use the data.

For synchronized streams, inspect timestamps and dropped-frame handling. If a provider offers hand trajectories, ask for coordinate definitions and an example transformation into your pipeline. Avoid accepting a list of modalities as proof that they are aligned.

Make task diversity measurable

Define a coverage matrix before asking for hours. Include task variants, objects, environments, lighting and performer variation relevant to your intended use. Ask whether similar clips repeat the same setup and how near-duplicates are tracked.

Keep evaluation examples separate from training material. Record the source and version of every delivery so you can investigate overlap later. Decide whether failed attempts, corrections and transitions belong in your dataset.

Inspect annotations in context

Use a representative sample to check whether task boundaries, action labels and success states are usable. Write down how ambiguous cases are handled and who adjudicates them. Ask for the provider’s QA process and rejected-example policy rather than relying on a general quality claim.

Do not postpone the rights discussion

Human recordings can include people, voices, screens and private locations. Ask how consent and permissions were documented, how incidental sensitive content is handled and what restrictions survive into your use of the data. Have the actual terms reviewed for your use case.

Use the egocentric-video filter for a first shortlist, the rights checklist for the contract discussion and the brief builder to define your required output.

Profiles to use in your research

For source-backed starting points, read Appen’s physical-AI profile, Objectways’ collection profile and DataX Power’s dataset profile. Use the recorded facts to prepare sample questions; the listings do not establish compatibility with your training pipeline.