Collect a production‑ready egocentric video dataset. In a day.
Describe your task, set your quality bar, and let 200,000+ contributors do the rest.
Trusted by Leading AI Teams
200,000+
Сontributors available
100+
Сountries
3 hrs
To first batch
No engineering
Required
The open web won't train your robot
The home is unstructured, unpredictable, and endlessly varied — and the open web doesn't contain the egocentric footage needed to train models that can handle it. Sourcing it yourself means recruiting contributors, building collection infrastructure, and validating results. Most teams don't have the bandwidth to do this fast.
Egocentric video example
The data infrastructure frontier labs use.
Now self-serve.
Getting egocentric video data for physical AI training shouldn't take months of fieldwork and custom infrastructure.
Toloka's self-serve platform gives you access to the same data collection infrastructure used by frontier AI labs — no sales cycle, no minimums.
How it works:
1
Describe the task.
2
The AI assistant configures the pipeline, selects contributors, and enforces quality constraints automatically.
3
LLM QA validates every submission before it reaches your pipeline — 89.1% accuracy catching failures.
Built with Toloka: HomER
To demonstrate what's possible, Toloka's own team used the self-serve platform to build HomER — an open-source egocentric robotics dataset spanning 17 household task categories.
The constraints were strict:
head-mounted camera required, both hands visible 95%+ of the time, no third-person footage, no re-used clips. LLM QA enforced every rule automatically at scale.
Task categories:
17
Total footage:
63 videos
Time to collect:
3 hours
Total cost:
$50
Trusted by Leading AI Teams
Ready to collect your own dataset?
Same infrastructure.
Your task categories.
Production-ready results in 24 hours.