Capture the real world your robot has to operate in.
Multi-sensor, real-world field capture for robotics and autonomy — RGB-D, LIDAR, IMU, force-torque and teleoperation, consented and India-resident.
A data-collection service for embodied AI: synchronized multi-sensor field rigs operating across real Indian streets, factories, farms, retail and homes — the messy environments lab data and Western datasets never cover. Consented, documented, and delivered training-ready.
- Modalities
- RGB Video, Stereo camera pairs, RGB-D / Depth (ToF), LIDAR / Point cloud, Radar, Event cameras, IMU, Force-torque, GNSS / RTK-GPS, Thermal, Audio, Teleoperation
- Languages
- India-wide field operations · 22 Indic for voice & UX context
- Quality bar
- IAA ≥ 0.85 · two-pass QA
- Onboarding
- NDA → SOW in 14 days
You can't ship a robot, a humanoid or an autonomy stack on data captured in a clean lab. Real deployments fail on the long tail — the unmarked speed bump, the cow on the highway, the cluttered factory aisle, the ambiguous hand signal — and that data is expensive and slow to gather at scale.
Building it in-house means standing up synchronized multi-sensor rigs, recruiting and training field crews, handling consent and privacy in public spaces, and calibrating everything so the streams actually fuse. Most teams burn months on logistics before a single training-ready frame lands.
We run the field operation end to end and hand you versioned, training-ready drops: calibrated, time-synchronized multi-sensor data with per-clip consent, provenance and capture metadata — plus a dataset card documenting environments, sensors, licensing and QA. Your team trains on day one instead of recruiting field crews for a quarter.
Where teams put this to work
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Robotics
Service & humanoid robots in real homes and workplaces
Egocentric and third-person multi-sensor capture of manipulation, navigation and human-robot interaction across Indian homes, offices and retail — the contexts global datasets miss entirely.
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Autonomy
Autonomy & ADAS edge-case capture
LIDAR + camera + IMU drives targeting the long tail of Indian road scenes — unstructured traffic, mixed signage, animals and informal road furniture — to harden perception where it actually breaks.
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Manipulation
Teleoperation & imitation-learning demonstrations
Human-in-the-loop demonstration data — control inputs, joint trajectories and outcomes — structured for imitation learning, policy bootstrapping and reward modeling.
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Industrial AI
Industrial & warehouse field data
On-site capture of picking, sorting, inspection and AGV navigation tasks on real factory and warehouse floors, with safety SOPs and trained field crews.
A pipeline built for SLAs
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1
Guidelines
Co-write annotation guidelines with the client team and pilot reviewers.
Calibrated rubric
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2
Pilot
Run a 500-unit pilot to validate guidelines and tooling.
IAA ≥ 0.80
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3
Production
Scale to full volume with daily QA sampling and weekly calibration.
IAA ≥ 0.85
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4
Delivery
Versioned drops with audit logs, kappa reports, and dataset cards.
Audit-ready
Quality gates
- Sensor calibration and time-sync verified before every capture session
- Consent, location and provenance logged per clip and scan
- Coverage and balance checks against the agreed capture matrix
- Per-batch capture-quality reports on every delivery
- India-residency option: collected, processed and stored in-country
At a glance
What does Physical AI Data Collection cover?
The technical spec — sensor modalities, coverage, deliverable formats and the QA stages every batch passes through.
| Specification | Detail |
|---|---|
| Modalities / sensors | RGB Video, Stereo camera pairs, RGB-D / Depth (ToF), LIDAR / Point cloud, Radar, Event cameras, IMU, Force-torque, GNSS / RTK-GPS, Thermal, Audio, Teleoperation |
| Coverage | India-wide field operations · 22 Indic for voice & UX context |
| Deliverable formats | Synchronized multi-sensor logs (RGB-D/LIDAR/IMU), calibration files, JSON metadata, consent records |
| QA stages | Rig calibration → field capture → sync & calibration check → metadata & consent validation → delivery report |
Representative spec. Exact sensors, formats and acceptance thresholds are scoped per project in the SOW.
What's included
A field-collection service built for embodied AI
We design the rig, run the field operation, and deliver training-ready multi-sensor data — consented and documented.
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Multi-sensor field rigs
Synchronized RGB-D, LIDAR, IMU, force-torque, GPS and audio streams, calibrated and time-aligned for clean fusion.
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Real Indian environments
Streets, factories, farms, retail and homes — the messy, uncontrolled contexts your robot actually has to handle.
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Teleoperation & demonstrations
Human-in-the-loop control inputs, joint trajectories and outcomes for imitation learning and policy bootstrapping.
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Consent & privacy by design
Documented field-ops SOPs, bystander opt-out and de-identification aligned with the DPDP Act, 2023.
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Provenance & dataset cards
Per-clip capture metadata, consent and license status, plus a dataset card documenting environments, sensors and QA.
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India-residency option
Collected, processed and stored in-country for government, BFSI and regulated buyers who need sovereign delivery.
/sovereign-data/
How it works
From scope to training-ready drops
A pipeline built for the realities of physical-world capture — calibrated, consented and QA-gated.
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1
Scope & rig design
Define sensors, environments, tasks and a capture matrix; calibrate and synchronize the multi-sensor rig.
Calibration verified
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2
Consented field capture
Trained crews collect across real Indian environments with documented consent and safety SOPs.
Consent + provenance logged
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3
Curation & coverage QA
Verify coverage and balance against the capture matrix; de-identify personal data and structure the streams.
Coverage checked
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4
Delivery & datasheets
Versioned drops with capture metadata, sensor-fusion-ready structure, and per-batch quality reports.
Audit-ready
Trust & compliance
Data you can defend in a procurement review
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Rights-cleared & consented
Written consent for capture in public and private spaces, with opt-out handling and per-clip consent references.
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DPDP-aligned & India-resident
De-identification SOPs for faces, plates and voices; optional in-country collection, processing and storage.
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Documented provenance
Every clip and scan tagged with capture metadata, location, consent status and usage license for clean downstream training.
FAQ
Physical AI data collection — common questions
- Which sensor modalities can you capture?
RGB and stereo video, RGB-D / depth (ToF), LIDAR / point clouds, radar, event cameras, IMU, force-torque, GNSS / RTK-GPS, thermal and synchronized audio, plus teleoperation logs. We design the rig to your training requirements and deliver calibrated, time-synchronized streams ready for sensor fusion.
- Can you capture teleoperation and imitation-learning data?
Yes. We capture human demonstration data — control inputs, joint trajectories and outcomes — structured for imitation learning, policy bootstrapping and reward modeling.
- How do you handle consent and privacy for public capture?
Every capture follows documented field-ops SOPs with consent handling, signage, bystander opt-out and de-identification — aligned with the DPDP Act, 2023. Provenance and license status are tagged per clip.
- Can the data stay in India?
Yes. We offer an India-residency option: collection, processing and storage performed entirely in-country for government, BFSI and regulated buyers. See our Sovereign Data page.
- We only need labeling, not capture — can you help?
Yes — that's our Physical AI Data Annotation service. We annotate physical and sensor data regardless of who collected it.
Need real-world data your robot can train on?
Tell us your sensors, environments and tasks — we'll scope a capture plan and a training-ready delivery schedule.
Ready to scope a pilot?
A senior Language PM will scope your project and respond within one business day.
Talk to a Language PM