Task definition
The action or workflow the model must learn, including objects, steps and desired outcome.
Example: pick mixed parcels from a tote and place them by category.Quantum D&R collects, annotates and processes real-world data for physical AI, AI and robotics. We run trained operators and instrumented robots, capture exactly the demonstrations a client needs, and deliver clean, labelled datasets ready for training.
You tell us what the robot should learn. We collect the data on your behalf.
* Goldman Sachs humanoid market forecast and Dubai Robotics & Automation Programme (u.ae). Market context, not company figures.
Quantum D&R is a data and robotics company in Dubai. We exist because the next wave of AI has to learn to act in the physical world, and it cannot do that without real demonstrations of physical work. That data does not exist on the internet. It has to be produced deliberately, by operating real robots and recording real people doing real tasks.
We act as a data collector on behalf of our clients. They tell us what their robot or model needs to learn, we define the task, capture plan and acceptance criteria, then collect, label and deliver the dataset.
We are an independent company that works closely with Quantum Tech Systems LLC, drawing on its integration, delivery and support experience in the UAE.
To become the region's source of high-quality physical-world data, so that any team building robots or physical AI can train on demonstrations captured to their own specification.
A future where general-purpose robots are put to real work across the UAE, trained on data collected and owned here rather than imported.
Physical AI is bottlenecked on real-world demonstrations, not compute. That data is what we produce, to each client's specification, and deliver ready to train.
You define the task and the schema. We run the operators and machines, capture the demonstrations, and hand over a clean labelled dataset.
Repeatable workflows, calibrated sensors and documented quality checks make every delivered episode useful, consistent and traceable.
You do not need to know how the data should be captured. You tell us what the robot or model must learn, and we handle the rest.
Describe what you want the robot to learn: the task, the environment, and how many demonstrations you need.
We agree the capture method, schema, volume, acceptance criteria and delivery format before any work begins.
Trained operators run the robots and suits in the lab, and every episode is annotated to your specification.
Clean, synced, quality-checked datasets delivered in the format your training pipeline expects.
Different tasks need different data. We match the capture method to what your model has to learn.
Operators drive humanoid and mobile robots through real tasks. Every joint movement, grasp and trajectory is recorded as demonstration data for imitation learning.
Instrumented suits capture how a person performs a task, recording motion and force directly from human demonstration for whole-body and manipulation training.
First-person camera rigs record the operator's viewpoint and hands, producing the egocentric video that vision-driven robot policies learn from.
Each service is designed around producing consistent, traceable and training-ready physical-world data.
Real-world demonstrations captured on your behalf, by teleoperation, sensor suits or head cameras, to the volume and schema you specify.
Labelling, syncing, cleaning and quality assurance that turn raw capture into training-ready episodes in your format.
Episode-level validation, sensor checks and acceptance testing against the quality rules agreed for your project.
Structured exports, documentation and metadata prepared for the storage and training pipeline your team already uses.
Open this section to see the six components we define with you before collection begins.
You do not need every answer on day one. We use these six components to turn your objective into a repeatable, quality-controlled dataset specification.
The action or workflow the model must learn, including objects, steps and desired outcome.
Example: pick mixed parcels from a tote and place them by category.The workspace, lighting, surfaces, people and edge cases that should appear in the data.
Example: warehouse aisle, variable daylight and partial occlusion.Teleoperation, sensor suits, first-person video or a combined setup matched to the task.
We recommend the method when you are unsure.The signals each episode includes, such as video, depth, pose, joint state, force or audio.
Sensor rates and synchronisation requirements are documented.The number and duration of episodes, plus the objects, operators and conditions to vary.
A pilot batch can validate the specification before scaling.Your annotation schema, quality thresholds, file structure, metadata and target format.
Delivered ready to connect to your training pipeline.Open for a concise view of leading robot platforms, VLA models, deployments and recent industry funding.
Robotics is shifting from machines programmed for one fixed motion toward systems trained on large, varied datasets. Humanoids are moving into factories and logistics sites, while VLA models connect visual understanding and language instructions directly to physical actions.
Figure 03 is the company’s current humanoid platform. Helix 02 extends its VLA system to full-body control, combining vision, touch and proprioception for long-horizon mobile manipulation.
Read the Figure update ↗Apptronik’s current Apollo 2 platform is offered with bipedal and wheeled bases. Its Robot Park facilities collect real-world work data to develop models with Google DeepMind.
Read the Apptronik update ↗A VLA model takes camera observations and natural-language instructions, then predicts robot actions. The goal is one adaptable policy that can understand new objects, tasks and environments instead of a separate program for every motion.
Explore Gemini Robotics ↗GR00T combines foundation models, simulation and synthetic-data workflows for humanoid development. NVIDIA’s 2026 reference design uses Unitree H2 Plus hardware, dexterous hands and Jetson Thor compute.
View the reference platform ↗Figure announced more than $1B in Series C commitments at a $39B post-money valuation in September 2025. Apptronik said its February 2026 extension brought its Series A total above $935M.
Figure funding ↗ Apptronik funding ↗Real demonstrations remain central: capture diverse tasks, train a policy, evaluate failures, then collect targeted examples. Synthetic trajectories can expand coverage, but real-world data anchors behaviour in physical conditions.
See NVIDIA’s data workflow ↗Industry snapshot updated August 2026. Company claims link to their original announcements.
Companies training physical-AI models and robot policies that need real demonstration data.
Teams building embodied systems that need representative data from real operating conditions.
Researchers and founders who need physical-world datasets without building a capture operation.
Share what you know today. We will help turn it into a clear capture, annotation and delivery specification.
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