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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.
Updated 2026-10-27 · 6 min read
World-model and physical-AI requests need a concrete task and observation specification. Continuous video can describe visual change; action-conditioned learning also needs the intended action representation. Do not imply that every human recording contains native robot controls.
This guide describes sourcing decisions, not a claim of available Fiund inventory or a guarantee about the hours needed to train a model. Test a relevant pilot before choosing collection volume.
Specify the prediction or policy task
State what is observed, what is predicted and which actions or states are required. Separate scene understanding, future-video prediction and robot-control learning. They may need different data and evaluation.
List task transitions, context before and after the action, failures and resets. Short edited highlights may omit the causal or temporal context needed by a particular objective; continuous recording still needs the right coverage.
Distinguish observations from control signals
A head camera can show human activity without recording robot actions. A wrist video may lack trajectories, gripper state or reference geometry. Ask which signals are native, reconstructed, manually labeled or unavailable.
UMI is a documented framework combining demonstrations with a policy interface. It illustrates why a method and its representation matter, rather than proving that any gripper video transfers directly to an arbitrary robot.
Inspect continuity and sensor relationships
Review episode boundaries, gaps, timestamps, calibration and derived depth or pose as required. State the clock domains and coordinate conventions. More camera-stream hours do not equal more independent behavior.
Use a representative pilot covering target objects, motion and failure cases. Test loading and task suitability, then agree acceptance and source-aware split boundaries before a larger order.
Scope a realistic request
Send a brief with the task, required views and signals, accepted units, environments, labels and pilot criteria. Ask whether an offered subset already exists or still requires recording or preparation.
Fiund confirms fit and availability after review. The standard buyer licence is non-exclusive and prohibits synthetic voice or likeness replication; a collection request does not override source restrictions or guarantee supply.
Frequently asked questions
Does this guide confirm a dataset is available?
No. Availability, permissions, included preparation and commercial terms require review of the actual proposed package.