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Egocentric Data Collection
Capture first-person training data for robotics using wearable camera systems across diverse settings like homes, factories, hospitals, and warehouses.
THE DATA ENGINEFOR PHYSICAL AI
Trusted by Clients
20+
∞
Collect training-ready datasets for robotics and embodied AI systems, powered by egocentric data, multimodal annotation, motion capture, and teleoperation.
○Google
○Amazon
○Microsoft
○ChatGPT
○Anthropic
○Meta
○Apple
○Nvidia
○Google
○Amazon
○Microsoft
○ChatGPT
○Anthropic
○Meta
○Apple
○Nvidia
About UsTRAINING DATA MADE FOR
TRAINING DATA MADE FOR
REAL-WORLD ROBOTS
Egocentric DataMultimodal AnnotationEmbodied AI
In robotics, your models are only as good as your data. At Robgence, we help robotics teams collect and annotate real-world training data across diverse environments, delivering egocentric, multimodal, and teleoperation datasets needed to build more capable embodied AI systems.
Our ServiceComplete Data Infrastructure
Complete Data Infrastructure
for Robotics and Embodied AI
From data collection to dataset delivery, we provide training-ready datasets robotics teams need to build, evaluate, and scale embodied AI systems.
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Multimodal Annotation
Transform raw recordings into training-ready datasets with detailed frame-level labeling, action segmentation, object tracking, intent annotation, and thorough quality validation.
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Human Motion Capture
Collect high-fidelity human motion datasets for robot learning, imitation learning, humanoid control, and embodied AI applications.
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Teleoperation Data
Generate observation-action datasets through remotely operated robotic tasks, enabling policy learning across diverse real-world scenarios.
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Video AI Datasets
Build custom video datasets for world models, robot perception systems, and vision-language-action models using real-world and synthetic data sources.
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Synthetic Data & Sim2Real
Scale training data with synthetic environments, digital twins, and sim2real pipelines that improve coverage while reducing collection costs.
Technology
Built to Collect Physical AI Data at Scale
Egocentric by Design
Capture first-person data from the perspective robots learn from. Our wearable capture systems combine video, depth, audio, and sensor streams into synchronized multimodal datasets.
Global Collection Network
Deploy data collection across a range of real-world environments like homes, factories, hospitals, warehouses, through a network of 20,000+ trained operators.
Training-Ready Delivery
From annotation and quality assurance to structured formats and cloud delivery, datasets arrive ready for robotics, embodied AI, and VLA training pipelines.
$0B+
Raised by humanoid robotics companies in 2024–2025
Market Size
0+
Egocentric & teleoperation demos per week, delivered to HuggingFace
Weekly Capacity
<0s
Average annotation label density per demonstration
Label Precision
0wk
From brief to policy-ready dataset delivery
Turnaround
Global Network
Real-World Data, Collected Around the World
1,000+Customers|
50+Cities|
5Continents
San Francisco
Mexico City
São Paulo
London
Kyiv
Cairo
Lagos
Nairobi
Karachi
Mumbai
Dhaka
Bangkok
Ho Chi Minh
Jakarta
Manila
Access a global network of trained operators collecting egocentric and multimodal data delivered ready for training and evaluation, across diverse real-world environments.
KitchensDomestic prep
StreetsUrban activity
WarehousesLogistics
WorkshopsCrafting
HomesResidential
OfficesWorkplace
Advancing the Future of
Physical AI with Robgence
Why Data Quality Matters
More Than Data Volume
Physical AI systems depend on diverse, real-world training data. Diversity of environments and interactions plays a more significant role than the size of your dataset.
Explore Insights08 February 2026Henry Leonardo
Developing Models That
Understand the Physical World
From egocentric video to multimodal sensor streams, embodied AI needs data that reflects how humans interact with objects, environments, and tasks.
Explore Insights12 February 2026Sarah Chen
Scaling Data Collection
Beyond the Lab
Global operator networks, annotation workflows, and quality assurance pipelines are becoming critical infrastructure for robotics teams training production-ready models.
Explore Insights18 February 2026Marcus Tan
Proof of Work
Real-World Data Collection, Proven At Scale
Large-scale, training-ready datasets delivered for the teams building the next generation of physical AI.
// EGOCENTRIC VIDEOS
500K+
Collected first-person robotics training data across homes, factories, hospitals, warehouses, and other real-world environments, leveraging our global operator network.
Explore Our Work// HUMAN ANNOTATIONS
2M+
Delivered frame-level action labels, object interactions, intent annotations, and multimodal labels built for robotics and Vision-Language-Action models.
Explore Our Work// HOURS OF TRAINING DATA
10K+
Built and delivered training-ready datasets encompassing egocentric capture, motion capture, teleoperation, and synthetic data workflows.
Explore Our Work0M+human annotations
0+active datasets
0+collectors worldwide
0+frontier lab partnerships
Backed byNVIDIAKhosla VenturesGeneral CatalystY Combinator
Our Focus Environments
Data collected where Physical AI operates
Private Homes
Capture everyday human interactions, object manipulation, and navigation behaviors in real residential environments.
Active Manufacturing Floors
Collect robotics training data from active production environments involving assembly, inspection, and material handling workflows.
Food and Beverage
Record human-object interactions, task execution, and service workflows across dynamic hospitality environments.
Offices
Capture navigation, workspace interactions, collaboration, and object usage in structured professional settings.
Warehouses and Logistics
Collect data for picking, packing, sorting, inventory handling, and autonomous workflow optimization in live logistics operations.
Farms and Greenhouses
Gather data from unstructured agricultural environments involving harvesting, inspection, crop handling, and field operations.
Data Coverage
for Real-World Robotics
Explore Data Collection→Indoor
Outdoor
Vehicles
Sports
Urban
Nature
Industry
People
The Bottleneck
PHYSICAL AI IS BLOCKED ON data,
NOT MODELS.
The best robotics teams use the same papers and the same compute. What separates production autonomy from impressive demos is the quality and realism of training data, captured where most teams cannot reach.
“Ten hours of curated factory data outperforms ten thousand hours of curated lab footage. Robots learn best from the environments they are expected to operate in.”
We help robotics teams collect, annotate, and deliver training-ready datasets from real-world environments, enabling more robust embodied AI systems.
Real-World Data Collection
Capture data from a range of environments, including homes, warehouses, factories, offices, farms, and other locations where Physical AI systems operate.
Multimodal Training Data
Collect synchronized streams of video, audio, motion, and sensor data to enhance robotics training, embodied AI, and Vision-Language-Action models.
Domain-Specific Annotation
Generate high-quality annotations designed for robotics workflows, including action labeling, object interactions, teleoperation, and motion data.
Training-Ready Delivery
Receive structured datasets that integrate directly into model training, evaluation, and deployment pipelines.
For Robotics Teams
Built for Robotics Teams. Delivered End-to-End.
From data collection and annotation to quality assurance and delivery, Robgence manages the entire data pipeline so your team can focus on training, evaluating, and deploying Physical AI systems.
Real-World Environments
Collect data across homes, warehouses, factories, offices, farms, and other environments where robots are deployed.
Egocentric & Multimodal Data
Capture first-person video, audio, motion, and sensor streams that enhance robotics training and embodied AI.
Training-Ready Annotation
Receive high-quality annotations for object interactions, actions, teleoperation, motion capture, and VLA model development.
Managed Data Operations
We handle hardware deployment, operator training, quality control, and dataset delivery from start to finish.
FROM REAL-WORLD INTERACTIONS
TO TRAINING-READY DATA
Explore how Robgence captures, annotates, and delivers training-ready datasets across real-world environments, powering the next generation of embodied AI systems.