Physical AI Data Annotation

Annotation for robot learning, embodied agents and egocentric & exocentric video - from spatial and temporal labels to robot action and language, delivered by expert human-in-the-loop teams.

Egocentric and exocentric data annotation for Physical AI
Ego + Exo
First & Third Person
Spatial + Temporal
Frame & Sequence Labels
Robot-Ready
Vision-Language-Action Data
Multi-Level QC
Human-in-the-Loop

What Is Physical AI Data Annotation?

Physical AI covers the models that have to act in the world, not just describe it: robot learning, embodied agents and human activity understanding. The datasets that train them need a layered annotation stack - spatial labels on frames, temporal labels on sequences, view-specific labels for egocentric and exocentric footage, and action and language labels that tie video to robot control.

Egocentric (first-person) footage carries fine hand and object detail and intent; exocentric (third-person) footage carries full-body pose and spatial context. Physical AI datasets are strongest when both are captured together, time-synced, and labeled with matching cross-view annotations.

HaiData annotates all of these layers with expert human-in-the-loop teams. Need the raw footage too? See our egocentric data collection services, or read our primer on egocentric vs exocentric datasets.

Egocentric vs exocentric view for Physical AI annotation

Physical AI Annotation Use Cases

The annotation behind the models that have to move, manipulate and understand real environments.

Robot Learning & Imitation

Annotate human and teleop demonstrations so manipulation policies can learn what a task looks like, step by step, from the actor's own point of view.

Vision-Language-Action (VLA)

Pair demonstration episodes with per-step language instructions so vision-language-action models can ground words in states, actions and outcomes.

Embodied AI & World Models

Label first-person video with motion, contact and state changes so world models learn how actions change the scene around an agent.

Human Activity Understanding

Action segments, keysteps and object state changes turn long-horizon tasks - cooking, assembly, repair - into supervised sequences.

AR/VR Assistants

Gaze, hand-object interaction and mistake tagging give first-person assistants the grounding to guide a wearer through a task in context.

Skill & Proficiency Assessment

Expert-vs-novice and proficiency labels, paired across egocentric and exocentric views, support technique, posture and quality analysis.

Egocentric and exocentric annotation use cases

Annotation Layers We Deliver

A layered stack for Physical AI datasets - spatial and temporal labels, view-specific labels for ego and exo footage, cross-view links, and the robot action and language layer that ties video to control.

Spatial Annotation

2D boxes, polygons and semantic, instance and panoptic segmentation of objects, tools, hands and surfaces; 3D cuboids and 6DoF object pose aligned to CAD or meshes; 21-keypoint hand pose and full-body pose (COCO, SMPL/SMPL-X); depth, point clouds, surface normals and camera pose and calibration.

Spatial annotation: 2D boxes and segmentation, 3D cuboids with 6DoF pose, keypoints and point clouds

Temporal Annotation

Action segmentation with start/end timestamps and verb-noun labels; keystep and procedural-step labels for long-horizon tasks; object state change (pre-state, point-of-no-return, post-state); anticipation labels; mistake and deviation tagging; and dense timestamped natural-language narrations.

Temporal annotation: action segments with verb-noun labels, keysteps and object state change

Egocentric-Specific

Hand-object interaction (hand boxes and masks, left/right, contact vs no-contact, active object); gaze and attention; egomotion and head-motion compensation; episodic-memory and visual-object queries; audio-visual labels (wearer vs bystander, diarization, transcription); proficiency; and privacy de-identification.

Egocentric annotation: first-person head-mounted camera view with bounding boxes for the left hand, right hand and object

Exocentric-Specific

Multi-person detection, tracking and re-identification across cameras; 3D full-body pose and mesh recovery with mocap-to-video alignment; human-object interaction triplets and scene graphs with spatial relations; environment mapping (floor plans, traversable areas, obstacles, semantic zones); and group and social activity labels.

Exocentric annotation: multi-person tracking with IDs, 3D body pose and human-object interaction

Cross-View (Paired Ego-Exo)

Frame-level temporal sync across all cameras; cross-view correspondence that matches the same object, hand or point between egocentric and exocentric frames; wearer identification in the exo views; and a shared world coordinate frame for every sensor.

Cross-view annotation: paired egocentric and exocentric frames with correspondence links and frame sync

Robot Action & Language (VLA)

Demonstration episodes as timestamped state-action pairs (joint angles, end-effector pose, gripper state); per-episode and per-subtask language instructions for vision-language-action (VLA) training; grasp annotation (points, grasp-type taxonomy, 6DoF poses); contact and force events; affordance labels; episode quality (success, failure, safety events); and scene VQA pairs.

Robot action and language annotation: language instruction, robot arm trajectory, 6DoF grasp and state-action readout for VLA

How HaiData Annotates Physical AI Data

Physical AI annotation is an operations problem as much as a labeling one: many layers, many sensors, and a quality bar that a single unchecked pass cannot meet. We run every project through domain-trained human-in-the-loop teams with multi-level and automated quality control, so each layer is reviewed against your acceptance criteria before delivery.

We annotate to your schema and to industry-standard formats - Ego4D-style narrations, Open X-Embodiment and DROID-style demonstration episodes, COCO and SMPL/SMPL-X pose - and deliver labeled data securely to your own cloud. Data handling is GDPR-aligned and aligned with India's DPDP Act 2023, with de-identification built into sensitive projects.

Explore our broader data annotation services and 3D point cloud annotation.

Why HaiData for Physical AI Annotation

Ethics, quality and control, built into every layer we label.

Domain-Expert HITL Teams

Trained human-in-the-loop annotators who understand robotics, embodied AI and first-person data, not a generic crowd guessing at hand-object contact or grasp poses.

Ego, Exo & Robot in One Place

Spatial, temporal, egocentric, exocentric, cross-view and robot action and language labels from a single partner, so your layers stay consistent and aligned.

Multi-Level & Automated QC

Every layer passes multiple levels of human review plus automated checks with configurable acceptance thresholds, so only labels that meet your bar are delivered.

Consent-First & Private

De-identification such as face, screen and document blurring, GDPR-aligned handling and alignment with India's DPDP Act 2023. ISO 27001 is in progress, expected 2026.

Format-Flexible

We annotate to your schema and to standard formats - Ego4D, Open X-Embodiment, DROID, COCO, SMPL/SMPL-X - so labeled data drops into your pipeline.

Secure Own-Cloud Delivery

Labeled datasets are delivered to your own cloud storage in the region you choose, with clear provenance and documented quality throughout.

Industries we Serve

AI is industry agnostic. So do we!

Automotive
Automotive
Healthcare
Healthcare
Agri Tech
Agri Tech
Retail
Retail
Warehousing
Warehousing
And More...

Related Services

Physical AI annotation pairs naturally with the rest of our stack. Explore our egocentric data collection services, human-in-the-loop services, 3D point cloud annotation, image and video annotation, and our full data annotation services.

Frequently Asked Questions

Physical AI data annotation is the labeling of datasets that teach models to act in the physical world - robot learning, embodied agents and human activity understanding. It is a layered stack: spatial labels on frames (boxes, segmentation, 3D pose), temporal labels on sequences (action segments, keysteps, state changes), view-specific labels for egocentric and exocentric footage, and robot action and language labels that tie video to control. HaiData delivers all of these through expert human-in-the-loop teams with multi-level quality control.

For egocentric (first-person) footage we annotate hand-object interaction (contact, active object), gaze and attention, egomotion, episodic-memory queries, audio-visual labels (wearer vs bystander, diarization, transcription) and privacy de-identification such as face, screen and document blurring. For exocentric (third-person) footage we annotate multi-person detection, tracking and re-identification across cameras, 3D full-body pose and mesh, human-object interaction triplets, scene graphs, environment mapping and group activity. We also handle cross-view labels when both viewpoints are captured together.

Yes. We annotate demonstration episodes as timestamped state-action pairs (joint angles, end-effector pose, gripper state) aligned to video, per-episode and per-subtask language instructions for vision-language-action training, grasp annotation (grasp points, grasp-type taxonomy, 6DoF grasp poses), contact and force events from tactile or force sensors when provided, affordance labels, episode quality (success, failure, recovery, safety events) and scene VQA pairs. We annotate to industry-standard formats such as Open X-Embodiment and DROID style layouts.

Yes. Hand-object interaction (hand boxes and masks, left/right, contact vs no-contact, active object), 6DoF object pose aligned to CAD models or meshes, 21-keypoint hand pose, full-body pose and SMPL/SMPL-X mesh, and gaze and attention labels from eye-tracker streams when provided are all part of our Physical AI annotation stack. These sit alongside 2D boxes and segmentation, 3D cuboids, depth, point clouds and camera pose.

Every project runs through expert human-in-the-loop teams with multi-level and automated quality control, so labels are reviewed against your acceptance criteria before delivery. On privacy, we apply de-identification such as face, screen and document blurring, our data handling is GDPR-aligned and aligned with India's DPDP Act 2023, and approved data is delivered securely to your own cloud. Our ISO 27001 certification is in progress, expected in 2026.

Yes. When a project captures the same task from first-person and third-person cameras, we annotate cross-view labels: frame-level temporal sync across all cameras, cross-view correspondence that matches the same object, hand or point between ego and exo frames, wearer identification in the exo views, and a shared world coordinate frame for all sensors. This is what lets a model learn a viewpoint-invariant representation of an action.

Ready to Annotate Your Physical AI Datasets?

Tell us your layers, formats and volumes and we will scope a Physical AI annotation project - egocentric, exocentric, cross-view and robot action and language. Write to us at info@haidata.ai to get started.

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