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Service

Robotics & Embodied AI

Corpshore AI captures robotics and embodied-AI training data, teleoperation, manipulation, real-world sensor and vision data for consumer and industrial robotics collected in controlled physical environments.

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97%+
accuracy via three-tier QA
Owned
physical collection space
1 SLA
capture and labeling together
50-70%
cost advantage vs US-domestic
What we deliver

Scoped, staffed, and QA-gated

  • Teleoperation and manipulation demonstrations
  • Real-world sensor and vision capture
  • 3D perception and grasp labeling
  • Controlled physical-environment data collection
TeleoperationManipulationSensor fusion3D visionGrasp data
Robotics & Embodied AI at Corpshore AI
Pain points we solve

The problems this service addresses

Imitation-learning and manipulation policies stall without enough clean demonstration data, which a platform cannot source because it has no physical space to capture it.
Demonstrations captured across inconsistent setups add noise, because lighting, fixtures, and objects drift between sessions.
Perception labels are outsourced separately from the raw capture, so handoff gaps appear between sensor streams and their annotations.
Custom manipulation tasks and edge cases cannot be reproduced on demand, so expanding coverage means rebuilding a rig each time.
Sensor calibration and framing vary session to session, so the same task looks different to the model across recordings.
Consumer and industrial robots need different rigs and scenarios, which a one-size-fits-all collection setup cannot serve.
Case scenarios

Where teams use this work

Illustrative examples of how this service fits real programs. They are representative use cases, not named clients.

A home-robotics company teaching manipulation

A team needs teleoperation demonstrations of a manipulator performing household tasks across repeatable scenes, with synchronized sensor and vision streams, so its imitation-learning policy has clean examples to learn from.

An industrial-inspection robotics team

A team needs sensor and vision capture of manipulation and inspection tasks in a controlled setup, plus 3D perception and grasp labels, delivered under one SLA so labels match the raw data.

A warehouse-automation startup

A team needs grasp and pick demonstrations across object types, layouts, and lighting held constant across sessions, so the policy generalizes rather than overfitting to one arrangement.

A service-robot vision team

A team needs 3D perception labels on captured scenes, with fixtures and objects reproducible on demand, so edge-case scenarios can be re-run to expand coverage without rebuilding the setup.

How we deliver

From scope to delivery, end to end

Step through the stages of a robotics & embodied ai engagement.

1. Scope robot, task, and sensors

Define the robot, target tasks, sensor suite, and scenarios so the rig and modality mix match the policy rather than a generic setup.

Stage 1 of 5
What to expect

How the engagement runs

  • It starts by scoping the robot, task, sensors, and scenarios, so the rig and modality mix match the specific policy rather than a generic setup.
  • Corpshore builds controlled scenes in owned physical space, including objects, layouts, lighting, and task scripts to your specification.
  • Capture and labeling stay under one SLA, so perception labels match the raw sensor and vision data without a handoff gap.
  • Sessions run to a written protocol covering sensor calibration, framing, and task steps, with per-session data checked against gold references.
  • You provide the robot or task definition, sensor requirements, and scenario intent; Corpshore provides the space, operators, capture, and labeling.
  • Because the environment is owned and repeatable, a scenario can be re-run to expand coverage without reconstructing the setup.
Insights

What doing this well requires

  • Robotics policies learn from clean, repeatable demonstrations, so controlling the capture environment matters as much as the volume of data.
  • Keeping capture and labeling under one operator removes the handoff gap that appears when perception labels are sourced separately from raw sensor streams.
  • Owned physical space is the operator advantage in robotics, because a scenario can be reproduced on demand rather than waiting for one to occur.
  • Consistent sensor calibration and framing across sessions is what lets a model see the same task the same way, which is the basis of generalization.
  • Consumer and industrial robots need scenarios scoped to the specific robot and task, not a shared rig, so coverage reflects the real deployment.
97%+
accuracy via QA cascade
35+
languages, native in-region
15,000+
seats across 18+ countries
50-70%
cost advantage vs US-domestic
Quality

Every unit passes a three-tier QA cascade

Tier 1

Annotator + peer review

Trained in-region annotators label to a versioned taxonomy. Every unit gets a structured peer check before it moves.

Catches ~80% of errors
Tier 2

Expert QA lead

Domain QA leads audit sampled and flagged work, resolve edge cases, and feed corrections back into annotator guidance.

Catches ~15% more
Tier 3

Programmatic + consensus

Automated consistency checks, gold-set benchmarking, and consensus scoring gate the batch before delivery.

Locks in 97%+ accuracy
FAQ

Robotics & Embodied AI, answered

Teleoperation and manipulation demonstrations, real-world sensor and vision capture, 3D perception and grasp labeling, and controlled physical-environment data for consumer and industrial robotics. Programs cover both the demonstration data that teaches a policy and the labeled perception data that grounds it.

Ready to scope a pilot?

Tell us your modality, volume, and languages. We'll return an indicative scope, timeline, and cost band.

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