HomER: Building an open-source egocentric robotics dataset with Toloka

Your robot will fail in deployment if your vision encoder learned from handheld smartphone footage instead of egocentric video. That's not a model problem as much as it's a data problem. The open web just doesn't have the high-signal, human-annotated data required to fix the problem. You need to generate real-world human data from scratch, which traditionally means weeks of guideline iteration and tedious quality control before a pipeline is even running.

To make this concrete, we'll walk through how we built HomER (Home Egocentric Robotics),  an open-source dataset of egocentric household task videos for robotics training, using Toloka's self-service platform. HomER spans 17 task categories (Kitchen & Food Handling, Cooking Sequences, Object Manipulation, Opening/Closing, and more) and is available on Hugging Face. The same pipeline that produced it can be applied to your own data collection needs.

Structuring task taxonomy with AI agents

The hardest part of bespoke data collection is often formalizing the task itself. It's not a case of dropping a prompt into a UI. You need to define a rigorous schema: what actions are in scope, how they're categorized, and how contributors should interpret edge cases.

Toloka solves this by integrating an AI agent directly into the project builder as a co-pilot for task design. For HomER, the high-level goal was to collect egocentric videos of people performing household tasks. The AI agent translated that goal into a strict activity taxonomy, specific action categories like "Kitchen & Food Handling," "Object Manipulation & Organization," and "Cooking Sequences", giving annotators a clear, unambiguous brief.

Curating a balanced data mixture like this is critical. Models trained on diverse distributions learn actual physical priors rather than memorizing brittle trajectories. A policy network over-indexed on a single action primitive won't generalize — it'll just memorize a specific motion pattern.

Deterministic constraints on human inputs

High-quality model behavior requires tightly constrained data. For embodied AI specifically, the capture perspective needs to match what the robot's hardware will eventually see. Toloka lets you encode rigid quality rules directly into the task definition.

For HomER, the constraints were the following:

• The camera must be head-mounted and facing forward.

• Both hands must remain visible in the frame for at least 95% of the video.

If a contributor uploaded handheld footage, even footage that otherwise looks correct, the system catches the violation based on your defined rules before it touches your dataset.

This matters more than it might seem. If your vision encoder learns to associate a household task with the distinct bobbing motion of a handheld smartphone rather than a stable egocentric view, the physical robot will fail to generalize during deployment. Codifying failure modes upfront, at the individual task level, is what makes it safe to scale the collection operation without flooding your pipeline with unusable noise.

Tapping a global workforce for fast iteration

Once your project schema is compiled, you need sheer throughput. Toloka routes your customized tasks to a verified crowd spanning over 100 countries and 40 languages.

For HomER, the platform matched the task criteria with over 1,800 active contributors immediately. Because the workforce operates globally, tasks are picked up around the clock and you can track completion rates in real-time. The latency of sourcing human-generated data drops from weeks to hours.

Scaling review with LLM-as-a-judge QA

Throughput is useless if your data is noisy, and manual review of hundreds of multi-minute videos is a significant bottleneck. Toloka embeds a proprietary LLM QA layer directly into the review workflow.

The LLM acts as an automated filter, evaluating every submission against your custom rubrics and accepting only those that match your pre-defined quality criteria. For HomER, that meant verifying head-mounted perspective, hand visibility thresholds, and correct task categorization — automatically, at scale. Submissions that don't meet the bar are rejected or flagged without requiring manual intervention.

Start building

Getting past the data wall shouldn't require engineering entirely new internal platforms. With agent-assisted task design and LLM-verified human inputs, you can construct the exact dataset your model lacks — in a fraction of the time.

• Explore HomER on Hugging Face: huggingface.co/datasets/toloka/HomER. Inspect the schema, categories, and video structure to see how the pipeline came together.

• Get started with Toloka's self-serve platform to launch your own data collection, or reach out to the team to discuss your specific training data needs.