Wearable and stereo multimodal human capture.
Multimodal Sensor Data
Multimodal Sensor Data for Physical AI and Robotics
Origin Data Lab combines visual, motion, audio, timing, device, and project-specific sensor context into structured datasets designed for robotics, embodied AI, multimodal learning, and model evaluation.
Real-World Context
Multimodal data connects what happened with how the system moved and sensed it.
Video alone may not capture motion state, timing, sensor context, or spatial relationships. Project-defined sensor streams add additional signals for learning, synchronization, validation, and analysis.
Fine-grained human motion and interaction signals.
Spatial and manipulation-oriented multimodal capture.
Core Requirements
Multimodal datasets require technical consistency across every stream.
Sampling, timestamps, file relationships, sensor schemas, calibration information, missing records, QA results, and manifest structure must remain traceable across delivery.
Video + IMU
Combine visual observations with accelerometer, gyroscope, orientation, motion, and device-level sensor context where available.
Audio + Context
Capture audio and operational context when sound, speech, machine state, or environmental cues are relevant to the target task.
Time & Synchronization
Preserve timestamps, sequence order, sensor timing, synchronization records, and device metadata for downstream alignment.
Production Architecture
From field capture to engineering-ready delivery.
Each project is scoped around the target task, capture architecture, metadata, validation criteria, quality requirements, and downstream integration needs.
Sensor Metadata
Define sensor type, sampling information, coordinate assumptions, device context, timestamps, and project-required metadata fields.
Validation & Exceptions
Check missing samples, malformed files, timing irregularities, metadata completeness, media integrity, and project-specific exceptions.
Structured Delivery
Deliver media and sensor files with manifests, schemas, QA records, documentation, and clear file relationships.
Related Capabilities
Explore connected Physical AI data capabilities.
FAQ
Multimodal Sensor Data FAQ
What sensor modalities can be included?
Depending on hardware and project scope, programs can include video, audio, accelerometer, gyroscope, device-motion signals, timestamps, GPS where applicable, stereo, wearable sensing, RGB-D, spatial data, and robotics-linked streams.
Can video and IMU be synchronized?
Synchronization requirements can be defined at project scope. Available timing precision depends on the capture hardware, sensor APIs, architecture, and technical requirements.
Do you provide sensor metadata and manifests?
Yes. Delivery can include sensor metadata, file relationships, timestamps, manifests, validation records, schemas, exception logs, and project documentation.
Can you use customer hardware?
Yes. Customer-provided sensor systems or capture devices can be evaluated and integrated when technically feasible.
Start with the Requirement
Tell us which signals your model needs.
Share the required modalities, sampling expectations, synchronization needs, metadata schema, hardware, environment, and delivery format to scope the capture architecture.