Human data for robotics models
Physical world data requires different infrastructure than text annotation. Toloka handles multimodal collection and temporal annotation so your robotics team can focus on model architecture and training.
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Training data from the physical world
Robotics models learn from video demonstrations, sensor streams, and human behavior in real environments. We run collection pipelines that capture the data at scale and return annotated datasets ready for your training loops.
Crowdsourced collection
Onsite collection
Annotation services
Quality systems
Robotics applications
Crowdsourced vs onsite collection
When to crowdsource
Your model needs to handle variability across homes, lighting conditions, and user behavior. Contributors record demonstrations in their natural environments. You get authentic diversity and edge cases that controlled setups miss. We apply centralized quality checks to ensure usability.
When to go onsite
Specifications require exact control over lighting, angles, expressions, or equipment. Professional operators work in dedicated facilities with calibrated hardware. Every frame matches your requirements. Reproducible conditions are maintained across the entire dataset.
Combining both
Many projects need baseline quality from controlled collection plus real-world diversity from crowdsourcing. We can run parallel pipelines that feed into unified annotation workflows.
How it works: collection to annotation pipeline
Capture phase
We source participants based on your demographic requirements, set up recording infrastructure (onsite or distributed), and monitor quality in real time. Failed segments trigger immediate retakes before participants leave.
Participant management:
Screening, scheduling, demographic verification, compensation handling
Technical setup:
Hardware calibration, lighting configuration, sensor synchronization, backup protocols
Live monitoring:
Frame quality checks, audio levels, angle verification, expression accuracy
Immediate fixes:
Retake protocols, on-the-fly adjustments, participant coaching, equipment troubleshooting
Validation phase
Raw footage goes through frame extraction, temporal consistency checks, and sensor alignment verification. Flagged segments route to expert reviewers who determine whether to approve, retake, or exclude.
Automated checks:
Schema validation, frame rate consistency, audio sync, metadata completeness
Expert review:
Complex sequences, edge cases, demographic verification, subjective quality assessment
Annotation phase
Annotators label according to your specifications. Frame-level object detection, temporal event boundaries, task success scoring, or preference rankings - whatever your training pipeline needs.
Quality calibration:
Annotators complete test sets, receive feedback, demonstrate consistency before production work
Delivery formats:
JSON, COCO, custom schemas - formatted for your ingestion pipeline
Partner with Toloka
Operational complexity you don't want
Physical data collection involves logistics that software teams aren't set up for. Participant recruitment networks. Site coordination. Equipment management. Local labor law compliance. Real-time problem solving when recordings fail. We handle this full-time so your team can stay focused on models.
Scale without headcount
Training runs are often in bursts. You need 10,000 videos this quarter, maybe nothing next quarter. Building an internal team for intermittent work doesn't make sense. We scale up for your collection windows and scale down between them.
What we bring to the table
Physical world expertise
We've run robotics collections across application areas. We know what fails in home environments versus studios. We've debugged POV angle issues, expression capture problems, and sensor synchronization failures enough times to anticipate them.
Quality without automation
Physical pipelines can't rely only on automated checks. Our quality systems combine protocol design, real-time monitoring, and expert review to catch problems before they compound across thousands of recordings.
Multimodal handling
Video, audio, sensor data, and metadata captured together and validated for temporal alignment. Annotations preserve relationships across modalities so your model sees coherent training signals.
Global participant access
Professional actors for expression and gesture work. Everyday people for natural behavior. Demographic diversity and geographic spread when your model needs to generalize.
Privacy and security
Home collection privacy
Face blurring, voice masking, metadata removal for consumer recordings. Your legal team approves all PII handling protocols before collection starts.
Facility security
Controlled access to onsite locations. Encrypted data transfer. Storage with audit trails and retention policies you specify.
Reproducibility
Version-controlled protocols, hardware configuration logs, complete documentation for replication or regulatory review.
Learn more about Toloka
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