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Mobile manipulation
Navigate, perceive and manipulate in dynamic warehouses — shelf picking, bin transfer and order assembly.
AMR + armPick & placeCollision-aware
Embodied AI
Data & Simulation Infrastructure for Embodied Intelligence
From real-world capture to simulation-grade synthetic data — DataBaker builds the data engine behind robots, autonomy and physical AI, closing the loop from the physical world to deployment.
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01 / The challenge
Physical AI is data-hungry — and the real world is slow
Robots learn by doing, but interaction data is scarce, expensive and dangerous to collect at scale. A single manipulation policy can demand millions of sensor frames, precise 3D labels and hundreds of rare edge cases that almost never occur in a real warehouse.
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multi-sensor frames captured
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robot platforms supported
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global collection & labeling
Real-world captureWarehouse · Logistics
02 / Capabilities
A full-stack data engine for embodied AI
One partner across the entire data lifecycle — capturing the physical world, turning it into machine-learnable signal, and reproducing it at simulation scale.
Live simulation
Pick & place
end-effector trajectory · 0 cycles
end-effector trajectory · 0 cycles
◉Multi-sensor capture
RGB-D, LiDAR, IMU, force-torque and tactile streams, synchronized and calibrated across fleets of real robots and human demonstrators.
✎Human-in-the-loop annotation
3D bounding boxes, semantic & instance segmentation, grasp points, action primitives and trajectory labels — reviewed by domain experts.
⛁Simulation & synthetic data
Digital twins of warehouses, factories and vehicles, with domain randomization to generate rare, dangerous and edge-case scenarios on demand.
↻Evaluation & closed loop
Model benchmarking, failure mining and active learning that feed hard cases back into the pipeline — continuously improving the policy.
03 / Pipeline
One closed loop — from the physical world to deployment
Capture, annotate, simulate, train and deploy. Every stage feeds the next, and field failures flow straight back to the beginning.
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Capture the physical world
Human demonstrators and production fleets record synchronized multi-modal streams in real environments.
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Turn signal into labels
Expert annotators add 3D geometry, semantics, grasp affordances and action primitives.
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Reproduce it in simulation
Digital twins with domain randomization multiply the dataset and manufacture rare edge cases.
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Evaluate and close the loop
Field failures are mined and re-enter the pipeline as new training signal.
04 / Applications
Where embodied intelligence goes to work
The same data engine powers mobile manipulation, industrial automation and autonomous driving — each with its own sensor mix, label schema and simulation stack.
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Industrial automation
Line-side manipulation, quality inspection and adaptive assembly with sub-millimeter grasp tolerance.
PalletizingInspectionAdaptive grasp
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Autonomous driving
Large-scale perception and prediction datasets, sensor fusion labeling and safety-critical scenario mining.
Sensor fusion3D perceptionScenario mining
05 / Impact
Data quality you can ship on
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annotation accuracy on 3D tasks
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countries with collection ops
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faster iteration vs. in-house
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synthetic frames generated
Get started
Let's build the data layer for embodied intelligence
Tell us about your robot, your sensors and the tasks you need to solve — we'll design the capture, labeling and simulation pipeline around them.