Train
train collectors and annotators
Robots need data quality and diversity before policy training. EgoArena turns raw egocentric video into training signal: bad clips filtered, coverage measured, reviewers tested, consensus weighted, and policies trained on the result.
84%
0.91
QADiversitymeasured
Frame continuityreal RLE at 30 fps
Curate verdictkeep high-signal window
Our customers have sold datasets made with us into
Keep your data from being a commodity by using our data quality & diversity platform.
train collectors and annotators
test workers continuously
certify top performers
detect worker vs team drift
relabel or repair bad labels and broken episodes
attach quality, safety, task, embodiment, contact, and dynamics metadata to raw data
score world-model readiness across synchronization, action-observation alignment, contact richness, object motion, latency, embodiment, and task success
curate silver datasets for your budget
train VLMs, robotics models, and forward dynamics models to label and score larger bronze datasets
benchmark which data mixes improve downstream robot policy performance
evaluate counterfactual rollouts through simulators, learned world models, or customer internal evaluators
recommend whether data should be kept, upweighted, downweighted, relabeled, recollected, or used for world-model calibration
EgoArena turns raw data into a map of what to keep, fix, train, and prove.
How EgoArena scores
Industry standard measures surface-level cleanliness
Generic QA catches bad labels and doesn't say what labels to update
Most companies accept or reject data, then label with basic VLMs
For dataset builders
Submit raw egocentric data. EgoArena turns noisy clips into ranked, reviewable signal.