Tasks and Steps
Episode boundaries, subtasks, critical actions, retries, interruptions, transitions, and temporal localization.
Core service / 04
Data Annotation for Robotics and Physical AI.
Turn existing video, image, and sensor data into model-ready supervision with a project-specific ontology, review method, acceptance criteria, lineage, and delivery schema.
Scoped annotation programs
Start With the Learning Signal and a Representative Pilot.
Use a pilot to test the ontology, edge cases, reviewer workflow, acceptance thresholds, lineage, and export against real assets before increasing volume.
Raw Evidence.
Model-Ready Labels.
Traceable QA.
The useful annotation is the minimum supervision your model or evaluation actually consumes—not the largest possible label set.
Direct answer
What Is Data Annotation for Physical AI?
Data annotation converts raw observations into structured targets a model can learn from or be evaluated against. For robotics and physical AI, that can mean temporal actions, object interactions, language, outcomes, failures, recovery, spatial labels, and the metadata that connects every label to its source asset.
A credible annotation program also defines ontology versions, ambiguous cases, reviewer roles, quality evidence, corrections, and the exact export the buyer will load. Labels without those controls are merely opinions in a file.
Read the Technical Annotation Guide ↗Annotation Types Defined Around the Model Objective
Select the smallest label stack that makes the target behavior, state, or evaluation observable.
Actions and Objects
Verb–noun actions, tools, objects, attributes, affordances, and project-specific categories.
Hands and Contact
Hand-object contact, active hand, manipulation phase, object state, and interaction events.
Goals and Narration
Instructions, goals, step descriptions, grounded narration, commentary, and language alignment.
Success and Recovery
Completion, partial success, failure, intervention, correction, recovery, and acceptance state.
Location and Motion
Bounding boxes, masks, keypoints, tracking, pose, or trajectories when the learning objective requires them.
A Pilot-First Annotation Workflow
Resolve definition and delivery risk before adding annotators or volume.
- 01
Specify
Name the model input, target output, task ontology, granularity, edge cases, and acceptance evidence.
- 02
Pilot
Annotate representative assets that include difficult boundaries, failures, and data-quality variation.
- 03
Annotate
Apply versioned instructions while retaining source IDs, methods, confidence, and review state.
- 04
Review
Sample or double-review according to risk, then adjudicate disagreements and correct the guidance.
- 05
Deliver
Validate the schema, lineage, files, documentation, and a buyer-facing loader or ingest test.
Quality Must Be Defined Per Labeling Task
Use observable rules that expose why a label passed, failed, changed, or required adjudication.
Ontology
Meaning
Definitions, inclusions, exclusions, hierarchy, null behavior, examples, and version.
Boundaries
Granularity
Clip, segment, event, frame, object, or sequence-level rules and allowable tolerance.
Agreement
Consistency
Reviewer agreement where it is meaningful, sampled review, error categories, and confidence.
Adjudication
Resolution
Escalation roles, disagreement evidence, guideline corrections, and supersession history.
Lineage
Traceability
Source asset, interval or object, annotation method, reviewer state, and transformation version.
Delivery
Usability
Schema validation, label coverage, splits, manifests, documentation, and buyer ingest testing.
Evidence boundary
Measured, Annotated, and Model-Derived Are Not the Same.
Sensor readings and source media are measured or recorded evidence. Human annotations interpret that evidence under a guideline. Model-generated pose, gaze, depth, force, trajectory, or label proposals are derived outputs.
Delivery records should preserve those distinctions, name the method and version, and state which checks were applied. Model assistance can accelerate a workflow; it does not become sensor ground truth through repetition.
Review Quality and Provenance Controls ↗What an Annotation Delivery Should Contain
The exact package is project-specific, but the definitions and evidence must travel with the labels.
- 01
Ontology and Guidelines
Label definitions, hierarchy, positive and negative examples, ambiguity rules, granularity, and version history.
- 02
Annotation Export
Stable asset relationships, intervals or coordinates, label values, review state, and the agreed schema.
- 03
Quality Evidence
Review method, sampled population, agreement where applicable, error classes, adjudications, and corrections.
- 04
Lineage and Versions
Source identifiers, annotation and model versions, transformations, superseded records, and release history.
- 05
Delivery Validation
Manifests, checksums, schema checks, documentation, known limitations, and a validated buyer ingest path.
Primary references
Research That Informs the Specification.
Data Annotation Questions
Short answers for teams evaluating a scoped robotics or physical-AI annotation program.
What data can EGXO annotate?
EGXO scopes annotation for existing video, image, and sensor data used in robotics and physical AI programs. A representative pilot confirms asset condition, rights, label feasibility, workflow, quality rules, and delivery format before scale.
Which annotation types are available?
Projects can include task and step boundaries, actions and objects, hand-object contact, object states, language, outcomes, failures, recovery, bounding boxes, masks, keypoints, tracking, and other model-specific labels. The final ontology and granularity are defined from the learning objective.
How is annotation quality measured?
The project brief defines observable acceptance rules such as guideline conformance, sampled review, reviewer agreement where appropriate, boundary tolerance, adjudication, completeness, lineage, and schema validation. EGXO does not reduce every annotation task to one universal accuracy score.
Can EGXO use model-assisted annotation?
Yes, when it improves the workflow and the buyer approves it. Model-generated proposals remain derived data: the model and configuration should be recorded, and human review or other validation must match the risk and acceptance criteria of the project.
How are annotation projects priced?
Pricing is scoped after reviewing representative assets, label definitions, granularity, expected volume, review depth, delivery requirements, and edge cases. A pilot is used to measure the actual workflow before a larger commitment.
Annotation brief
Start With Representative Data and the Target Label.
Share what data already exists, what the model must learn or predict, the required output format, and the acceptance criteria that matter.
Scope an Annotation Pilot