Physical AI Data Collection
Why Innodata
Innodata helps teams move from raw visual, sensor, and interaction data to validated, model-ready datasets for training, evaluation, retraining, and deployment.
Capabilities
Innodata provides custom image, video, sensor, egocentric, and real-world interaction data collection designed around your model’s exact requirements.
First-person video captured through body-worn cameras, AR/VR headsets, wearable devices, and real-world task environments.
Synchronized sensor data including IMU, depth, RGB, IR, LiDAR, aerial, egocentric, and wearable capture.
Teacher-follower demonstrations, teleoperation logs, task completion sequences, hand-object interaction, household workflows, workplace workflows, and environment metadata.
Image and video capture, object detection and tracking, action and motion labeling, scene understanding, small-object detection, geospatial imagery, aerial imagery, and egocentric video labeling.
Every program is scoped around your model requirements, collection environment, contributor needs, quality thresholds, and delivery format.
01
Define modalities, task taxonomy, collection environments, metadata schema, quality thresholds, and delivery format.
02
Utilize SMEs, voice talent, operators, trained data specialists, or domain experts based on language, geography, demographics, expertise, and task requirements.
03
Manage studio, remote, lab, onsite, hybrid, and in-the-wild workflows with moderation, support, and protocol adherence.
04
Add transcripts, labels, timestamps, metadata, QA scores, preference data, evaluation outputs, and validation reports.
05
Provide structured, secure, ingestion-ready data aligned to your model pipeline.
* All sensitive data types — including biometric, facial, and healthcare-adjacent data — are collected through consent-managed, privacy-compliant workflows with GDPR, HIPAA, and client-specific data governance requirements.
Why Innodata
We deliver precisely annotated frames, custom keypoints and skeletal models, QA scores, metadata, and validation reports ready for model training and evaluation.
Our workflows combine automation, expert human annotators, pre-labeling models, custom automation pipelines, and multi-layer QA.
For motion-heavy Physical AI data, Innodata uses motion analytics and kinematic modeling to track skeletal movement, identify abrupt or impossible motion patterns, flag potential labeling anomalies, and improve dataset consistency before training begins.
Innodata supports large, continuous annotation campaigns, processes thousands of frames per night, and provides turnaround measured in hours for annotation workflows.
Innodata supports advanced AI teams, including leading hyperscalers and frontier AI organizations, across high-volume training data, evaluation, and quality assurance workloads.
Programs
Robotics data collection, embodied AI data collection, VLA model training data, world model training data, teleoperation datasets, teacher-follower demonstration datasets, manipulation data, navigation data, robotic workflow data, and other real-world interaction datasets.
Egocentric video data collection, wearable AI data collection, first-person video datasets, human-object interaction datasets, task completion sequences, household workflows, workplace workflows, environment metadata, and other in-the-wild data collection programs.
Computer vision data collection, sensor fusion datasets, image and video annotation, geospatial and aerial imagery annotation, autonomous systems data, sports kinematics analysis, agriculture datasets, livestock monitoring datasets, and other domain-specific image, video, and sensor datasets.
Need something more specific? Innodata can design a custom collection program around your model requirements, modalities, task taxonomy, collection environment, contributor needs, quality thresholds, and delivery format.
Skip the collection timeline. Innodata offers pre-built, fully consented datasets available for immediate licensing, collected to the same quality standards as our custom programs.
Every OTS dataset ships with full provenance documentation, consent records, and metadata schemas. Custom extensions available when off-the-shelf coverage isn’t enough.
First-person capture across Household, Blue Collar, and Hobbyist & Craft task domains, with synchronized IMU sensor data, task annotations, and environment metadata.
Studio-quality recordings across 120+ languages and dialects for ASR, TTS, and voice AI training.
Structured documents, expert Q&A, and domain-specific corpora.
Get Started
Tell us what you are building, what data your model needs, and where your current datasets are falling short. Innodata can help you scope a pilot, design the collection workflow, and scale the program into production.
Physical AI data collection is the process of gathering real-world interaction data used to train models that perceive and act in physical environments. This data can include egocentric video, sensor streams, teacher-follower demonstrations, teleoperation logs, human-object interaction sequences, task workflows, and environment metadata.
Innodata collects egocentric video, wearable capture, IMU sensor data, depth and RGB data, human-object interaction data, task completion sequences, teleoperation data, teacher-follower demonstrations, image data, video data, and environment metadata.
Yes. Innodata collects and annotates Physical AI data for vision-language-action models and world models, including egocentric video, sensor streams, human-object interaction sequences, task workflows, teleoperation logs, and teacher-follower demonstrations.
Yes. Innodata provides annotation, enrichment, QA, validation, metadata, labels, transcripts, timestamps, QA scores, and secure model-ready delivery. For motion-heavy data, Innodata also uses motion analytics and kinematic modeling to improve dataset consistency before training.
Yes. Innodata designs custom collection programs based on modality, task taxonomy, contributor requirements, collection environment, metadata schema, quality thresholds, and delivery format.
Off-the-shelf datasets are useful when your model needs broad coverage of common tasks or environments. Custom data collection is the better fit when your model requires specific devices, demographics, task taxonomies, edge cases, or proprietary formats.
