DataoceanAI helped an embodied AI company focused on world model training transform real-world data collection from project-based execution into a standardized, scalable, and continuously operating production system.
Project Highlights
50,000+ hours/month
of high-quality usable data supported at scale
Real-World Environments
Home, office, retail, and industrial settings
Multiple Collection Paradigms
UMI, first-person data collection, and robot teleoperation demonstrations
The Challenge
An embodied AI company focused on world model training needed multimodal data captured in unstructured real-world environments through UMI and first-person collection methods.
The customer required a data system capable of covering diverse scenarios, tasks, and interaction patterns, while supporting continuous high-quality data supply for long-term model training and optimization.
The challenge was not simply to collect more data.
Data collected in controlled laboratory environments could not fully reflect the complexity of real-world conditions. Variations in lighting, object occlusion, material properties, and unpredictable interaction paths created a clear gap between laboratory data and open-world environments.
At the same time, multimodal data generated during high-frequency interactions needed millisecond-level temporal alignment, placing higher demands on system engineering.
Traditional project-based data production also lacked unified standards and engineering SOPs. Collection, annotation, and quality assurance were often fragmented, making it difficult to build a scalable and repeatable production capability.
The Solution
Expanding Collection into Real-World Environments
DataoceanAI deployed distributed collection resources across multiple locations, extending data collection beyond laboratories into homes, offices, retail environments, and industrial settings.
Natural lighting changes, diverse objects, and real-world interaction behaviors were incorporated into the collection process, helping the resulting data better reflect real-world physical environments.
Supporting Multiple Collection Paradigms
The production system supported three complementary collection approaches:
- UMI-based collection
- First-person data collection
- Robot teleoperation demonstrations
Together, these methods enabled the project to support data needs ranging from lower-cost, large-scale collection to more precise and complex manipulation demonstrations.
Unifying the Data Engineering Workflow
DataoceanAI integrated task scheduling, collection execution, cross-modal alignment, annotation, data processing, and delivery into a unified data engineering platform.
This enabled more consistent management and synchronized processing of multimodal data across the production workflow.
Automated checks, human review, and feedback loops were combined to control synchronization accuracy and data consistency throughout production.
Standardizing Production for Scale
Standardized SOPs and a distributed resource network were introduced to support the expansion of collection capacity as demand increased.
This allowed the production system to move more smoothly from pilot projects to large-scale operation without rebuilding the workflow for every new task.
The Results
As the program entered scaled operation, the system supported a stable supply capacity of more than 50,000 hours of high-quality usable data per month.
The project also demonstrated the feasibility of scaling UMI and first-person data collection in complex real-world environments.
More importantly, real-world data production evolved from a highly customized engineering task into a more standardized, repeatable, and continuously operating production mechanism.
This provided the customer with a more sustainable data foundation for ongoing world model training and embodied AI development.
Build Embodied AI on Scalable Real-World Data
From distributed real-world data collection and multimodal synchronization to annotation, quality assurance, and large-scale delivery, DataoceanAI provides end-to-end data engineering support for embodied AI and world model development.
Talk to DataoceanAI about your next embodied AI data project.
