22 guides
Frontier modalities
Sourcing egocentric video, agentic trajectories, world-model, and far-field data.
Guides in this topic
5 min read
Egocentric video versus teleoperation versus UMI data
Separate observations of human activity from robot actions and gripper-based demonstrations before choosing training data.
5 min read
Egocentric data collection pay by program and country
Check dated official program terms, eligibility and billing units without turning unrelated job rates into footage prices.
5 min read
Recording workplaces for AI: permissions and preparation
Review site access, workers, bystanders, confidential material and intended use before recording a workplace for a dataset.
5 min read
Egocentric data providers: an evidence-based comparison
Compare catalog claims, custom sourcing, closed-loop programs and technical evidence rather than ranking unverified inventory.
5 min read
Off-the-shelf versus custom egocentric collection
Choose an existing subset, new recording or a hybrid only after reviewing task coverage, missing signals and pilot cost.
5 min read
Narrated egocentric data: collect useful task descriptions
Keep spoken narration, transcripts, action segments and generated descriptions distinct and linked to the original timeline.
5 min read
Hand-pose annotations: measured versus estimated labels
Inspect reference systems, coordinates, uncertainty and processing before treating hand pose as ground truth.
5 min read
A pilot acceptance plan for robotics data
Write acceptance, source-aware split boundaries and rework rules before expanding a robotics collection.
5 min read
Commercial video datasets: buying and evaluation checklist
Evaluate source coverage, duplicates, originals, permitted use and delivery acceptance before buying a video collection.
6 min read
Monocular versus stereo versus RGB-D egocentric data
Choose camera observations and depth deliverables by the model task, calibration evidence and actual output files.
6 min read
Head and wrist cameras: what each view adds
Plan complementary egocentric viewpoints without mistaking moving head/wrist cameras for a fixed stereo pair.
6 min read
Egocentric camera calibration and synchronization checklist
Audit geometry and time independently, then connect the evidence to camera settings, sessions and delivery files.
6 min read
Recorded hours versus accepted hours versus camera-stream hours
Normalize recording quantities before comparing collector pay, dataset prices or collection budgets.
6 min read
Egocentric recording earnings calculator
Estimate gross compensation, expenses and net return per labor hour using your own assumed rate and acceptance level.
6 min read
Custom robotics data collection budget worksheet
Build a pilot-first budget that separates setup, collection, acceptance, annotation and licensing instead of multiplying a headline recording rate.
6 min read
Egocentric video rejection reasons and preflight QA
Find capture failures before scaling, document rejected segments and keep the acceptance policy tied to the intended task.
6 min read
Egocentric dataset datasheet and sample manifest
Connect episodes, streams, calibration, timestamps and permissions in a reviewable package without claiming missing evidence exists.
6 min read
Stereo egocentric data pricing: compare the complete scope
Compare stereo dataset quotes using accepted activity hours, calibration, synchronization, annotations and delivery requirements rather than camera count alone.
6 min read
Egocentric video datasets: a buyer’s guide
First-person wearable-camera video for robots, world models, and assistants. What to specify, and why rights are the hard part.
6 min read
Sourcing data for world models and physical AI
World models learn to predict what happens next. They need long, continuous real-world video tied to actions and sensors, with clean provenance.
6 min read
Agentic trajectory and computer-use datasets, explained
Agents learn from recorded task sequences: what was on screen and what the user did. Here is what a trajectory contains, and why consent and PII decide whether you can use it.
6 min read
Far-field and diarization audio data
Meeting and in-room speech is distant, overlapping, and multi-mic. Training robust ASR and diarization needs that acoustic reality, captured with consent.
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