Sell your data for 10x more

Robots Need Data Quality & Diversity

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.

Egocentric robot pick-and-place clip representing dataset coverage scoring84%
Egocentric cup-handling clip representing consensus-weighted review quality0.91
Egocentric keyboard task clip representing label QA reviewQA
CurateSegment hands, objects, and contact regions before a clip enters the dataset.

Diversitymeasured

Frame continuityreal RLE at 30 fps

Curate verdictkeep high-signal window

Our customers have sold datasets made with us into

12datasets evaluating
102,840+clips reviewed
10,119hours reviewed

Sell your data for 10x more

Keep your data from being a commodity by using our data quality & diversity platform.

Dataset utility mapworker quality, metadata, model evidence

Train

train collectors and annotators

Test

test workers continuously

Certify

certify top performers

Drift

detect worker vs team drift

Repair

relabel or repair bad labels and broken episodes

Metadata

attach quality, safety, task, embodiment, contact, and dynamics metadata to raw data

Readiness

score world-model readiness across synchronization, action-observation alignment, contact richness, object motion, latency, embodiment, and task success

Curate

curate silver datasets for your budget

Train Models

train VLMs, robotics models, and forward dynamics models to label and score larger bronze datasets

Benchmark

benchmark which data mixes improve downstream robot policy performance

Rollouts

evaluate counterfactual rollouts through simulators, learned world models, or customer internal evaluators

Recommend

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

Every clip gets a training-signal score.

We score downstream usefulness

Industry standard measures surface-level cleanliness

We catch bad robot training signal

Generic QA catches bad labels and doesn't say what labels to update

EgoArena routes and improves data

Most companies accept or reject data, then label with basic VLMs

For dataset builders

Prove your datasets are worth training on.

Submit raw egocentric data. EgoArena turns noisy clips into ranked, reviewable signal.

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