Built for Real-World Robotics
We collect data from the places where robots actually work, like homes, warehouses and factories, improving real-world performance.
About UsPowering the data infrastructure
Powering the data infrastructure
behind Physical AI
Robgence assists robotics teams collect, annotate, and deliver training-ready datasets for embodied AI systems that interact with the world. Through egocentric data collection, multimodal annotation, motion capture, and teleoperations, we provide the real-world data needed to train, test and deploy scalable Physical AI.
20,000+Trained Operators
50+Cities Worldwide
5Continents
< 2 WeeksDataset Delivery
End-to-End Data Infrastructure
From data collection and annotation to quality assurance and delivery, we manage the entire data pipeline so teams can focus on model development.
Trusted by Physical AI Teams
Our global operator network, structured workflows, and training-ready datasets help robotics teams scale faster than building them in-house.
Our StoryBuilt from the
Built from the
realities of deployment
Robgence was developed from a simple observation: the biggest challenges in Physical AI don’t emerge in controlled environments, they emerge in the real world where robots and embodied AI systems are deployed. Working alongside businesses running everyday operations, we saw firsthand how difficult it was to access the data needed to train reliable robotic systems.
This shaped our approach towards building around real environments and real-world complexity. Today, Robgence helps robotics teams bridge the gap between research and deployment through the data infrastructure that Physical AI requires.
FAQ
About Robgence
Common questions about what we do, who we work with, and why Physical AI teams choose Robgence.
Get in touchWhat does Robgence do?
Robgence provides end-to-end data infrastructure for Physical AI and robotics teams. We collect, annotate, and deliver training-ready datasets — including egocentric video, multimodal sensor streams, motion capture, and teleoperation recordings — for teams building embodied AI systems that interact with the physical world.
What is Physical AI?
Physical AI refers to AI systems that perceive and act in the physical world — robotic manipulators, humanoid robots, autonomous mobile robots, and embodied AI agents. Unlike purely software-based AI, Physical AI requires real-world training data collected in the environments where these systems operate.
Who does Robgence work with?
Robgence works with robotics research teams, Physical AI startups, and enterprise automation companies that need high-quality real-world training data. Our clients include teams building humanoid robots, manipulation systems, autonomous mobile robots, and vision-language-action (VLA) models.
What makes Robgence different from generic data annotation companies?
Robgence is purpose-built for robotics. Every collection protocol, annotation schema, and dataset format is designed from the ground up for robot training — not repurposed from generic data pipelines. We operate a global network of trained operators, support synchronized multimodal capture, and deliver in formats natively compatible with leading robotics AI frameworks.
What real-world environments does Robgence collect data in?
Robgence collects data across the environments where robots are actually deployed — manufacturing floors, warehouses, domestic homes, healthcare facilities, hospitality venues, and office spaces. We capture data in genuine operational conditions rather than controlled lab settings.
How does Robgence compare to open-source robotics datasets?
Open-source datasets like Open X-Embodiment and DROID provide valuable benchmarks but are fixed in scope, environment diversity, and modality. Robgence delivers custom datasets scoped to your specific robot, task, and environment — with synchronized multimodal capture (RGB, depth, IMU, force-torque), domain-matched conditions, and delivery in formats your training pipeline already uses. For teams that need data precisely tuned to their deployment context, Robgence is purpose-built where open-source datasets are general-purpose.
How quickly can Robgence deliver a dataset, and what is the process?
Robgence delivers pilot datasets within days and production-scale datasets in under two weeks. The process starts with a scoping call to define task, environment, modality, and annotation requirements. Our pre-trained operator network then collects data in the target environment, which flows through quality-controlled annotation pipelines before delivery in your chosen format — RLDS, MCAP, ROS 2 bag, or custom.