01 · Core service
Real-world data collection
Task-based collection with real operators, objects, tools and environmental variation inside approved commercial settings.
Explore field operationsEmbodied AI data · Real-world operations
Real-world data for robots that work.
From commercial field collection and synchronized capture systems to annotation and traceable quality assurance, RoboMetrics turns real human work into model-ready data.
One managed data operation
Human capability, captured with evidence.
RoboMetrics connects real environments, qualified operators, task-specific capture systems, annotation teams and quality controls into one data production workflow.
We do not treat collection as simply recording video. Every program begins with the capability a robot needs, then defines the task, viewpoints, signals, labels and acceptance evidence required to train it.
- 01Environment
- 02Task design
- 03Capture
- 04Inspect
- 05Annotate
- 06Deliver
Capabilities
The complete path from work to data.
Four production capabilities available today, with a research path toward model evaluation and evaluation-guided data optimization.
02 · Core service
Capture systems & integration
Monocular, synchronized multi-view, egocentric, depth and multimodal configurations designed around each task.
Explore capture systems03 · Core service
Data quality assurance
Documented controls before, during and after capture, with issue handling, rework and version traceability.
See the quality system04 · Core service
Embodied data annotation
Task-aware labels connecting actions, objects, states, contact, outcomes and recovery behavior.
Explore annotation
Packing task · Wearable capture01 · Real-world collection
Capture real work, not staged demos.
The data reflects the environments robots will eventually enter — including changing objects, occlusion, space constraints and imperfect conditions.
- 01Qualify the environment
Confirm access, task value, safety, privacy and operating constraints.
- 02Design the task
Define actions, tools, variables, exceptions and acceptance conditions.
- 03Operate in the field
Coordinate people, devices, sessions, progress and issue recovery.
- 04Scale with control
Move from a verified pilot to repeatable collection without losing traceability.
02 · Capture systems
The right sensors for the task.
We design and integrate synchronized capture configurations around the behavior, environment and evidence a model needs.
VISIONMOTIONROBOT STATESYNC
Monocular & multi-view vision
Single-camera scale or synchronized external views for spatial context, occlusion and coordinated movement.
Egocentric & hand-object views
Head, chest or close-range viewpoints for hand-eye coordination, tool use and fine manipulation.
Stereo, RGB-D & depth
Depth maps or point clouds when distance, geometry, grasping and spatial reconstruction matter.
Motion, force & robot signals
IMU, pose, force, torque, tactile, joint, gripper and teleoperation logs aligned to the same task timeline.
Capture orchestration software
Device health, calibration, timestamps, task sessions, metadata, secure transfer and data lineage.
03 · Quality assurance
Quality is controlled at every handoff.
A broken timestamp, missing view or wrong label can multiply training cost. Quality starts before the first frame is captured.
BEFORE01
Define & verify
- Task protocol
- Device calibration
- Sample approval
- Acceptance rules
DURING02
Monitor & correct
- Frame and exposure checks
- Synchronization health
- Task conformance
- Issue and recapture loop
AFTER03
Inspect & document
- Completeness checks
- Human review
- Version traceability
- Quality report
Delivery evidence
Dataset versionTask coverageSensor completenessIssue logAcceptance status
04 · Embodied annotation
Structure what happened, how it happened and why it mattered.
Raw sensor records become useful when actions, objects, states and outcomes are connected to the task.
Each delivery can include an ontology, data dictionary, annotation specification, version history, coverage statement and quality report.
STEPReach → grasp → place
OBJECTParcel / target shelf
CONTACTRight hand · stable
OUTCOMECompleted
Field environments
Reality is part of the dataset.
Our first field network is rooted in Indonesia, connecting diverse commercial environments with the tasks robots need to learn.
4thLargest population worldwide
7Priority environment categories
01Field network, built to expand
RetailLogisticsFood serviceManufacturingHospitalityAgricultureMining
Research horizon
From more data to the right data.
Our research direction connects measurable model gaps to the next data program. These capabilities are in development.
05In development
Model evaluation research
Task-based evaluation across success, precision, robustness, generalization, recovery, efficiency and safe behavior.
06In development
Evaluation-guided data optimization
Map failure patterns to missing scenarios, difficult examples, sensor configurations and targeted recollection.
- Evaluate
- Diagnose
- Collect
- Re-test
The larger idea
A field university for humanoid intelligence.
A robot learns like a professional: through real tasks, the right tools, reliable instruction, disciplined practice and measurable evaluation.
RoboMetrics brings these ingredients together as infrastructure for transferring human capability into machines.
01Field campuses
Commercial environments
02Curriculum
Structured task programs
03Faculty
Operators and experts
04Laboratories
Capture, QA and annotation
05Research
Evaluation and learning loops
Start a program
What should your robot learn next?
Tell us the task, environment, sensor requirements and target data volume. We will help define a practical pilot.
contact@robometrics.ai