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The Annotation Layer Behind Physical AI

Multimodal, autonomous vehicles, and embodied agents that need to perceive, reason, and act in the real world, like your other production AI data
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Three Roles, One Execution Layer

Whether you're managing timelines, validating output, or watching spend, the platform works the way your role needs it to.
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Multimodal Annotation
Image, video, LiDAR, radar, IMU, GPS, and audio, annotated in one platform
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2D/3D Labeling
Bounding boxes, polygons, and point cloud annotation across 2D and 3D data.
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Segmentation
Semantic and instance segmentation for scene and object-level understanding.
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Keypoint & Pose Annotation
Keypoint and pose estimation for both human and robot subjects.
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Path & Depth Annotation
Lane, road, and path annotation, plus depth and occupancy labeling.
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Tracking & Events
Object tracking across frames, plus event, action, and scene classification.

Sensor Fusion for a Unified View of the Physical World

Synchronize and annotate data from multiple sensors like cameras, LiDAR, radar, for sensor fusion that gives perception models one consistent read on the environment.
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Synchronized Multi-Sensor Annotation
Cameras, LiDAR, radar, and other sensors annotated in alignment, not in isolation.
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Time-Series Sync
Sensor streams aligned in time, not just in space.
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Calibration & Multi-View
Calibration visualization, multi-view annotation, and sensor overlay/alignment tools.

Built for How Robots Actually Move and Act

Annotation tooling tailored to navigation, manipulation, localization, and human-robot interaction.
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SLAM & Localization
Support for SLAM and localization workflows.
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Trajectory & Pose
Trajectory, path, and robot pose labeling.
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Manipulation & Grasp
Manipulation and grasp-point annotation.
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Obstacle & Navigation
Obstacle, free-space, and navigation-map annotation.
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Human-Robot Interaction & Dynamic Behavior
Human-robot interaction labeling and dynamic object behavior annotation, for systems that share space with people.

The Execution Layer for your Labeling-Ops

A unified execution layer that combines auto annotation, domain-qualified experts, and adaptive workflows to consistently deliver production-ready outcomes.
AI Pre-Labeling
Pre-labeling that runs on task-matched models:  SAM-3 for segmentation, YOLO v12 for detection, Mistral OCR for document extraction, Whisper-V3 and Google STT v2/Chirp for audio, with confidence-scored outputs routed to human review only where the model is uncertain. Swap models per task without re-architecting your pipeline.
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Workflow Orchestration
Task allocation runs on model/reviewer affinity, not a flat queue, each task goes to whichever model or specialist is calibrated for it. Maker-Checker and Consensus review sit on top as configurable QC layers, and every decision is versioned and traceable back to the model or reviewer that made it.
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Qualified Human Expertise
Specialists are qualified per task type before they touch your data: assessed, trained, certified, then monitored against live accuracy metrics, with a defined path to upskill into harder edge cases. Reviewers aren't generalists reassigned across projects; they're matched to the data type they were certified on.
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Sensor Fusion for a Unified View of the Physical World

The same labelops execution layer adapts to the vertical, not the other way around.
Manufacturing · Warehousing & Logistics · Supply Chain · Surgical Assistance · Hospital Operations · Defense · Precision Farming · Autonomous Harvesting · Construction · Autonomous Vehicles · Autonomous Mining · Urban Maintenance
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LiDAR Robotics
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Proof point for the Autonomous Vehicles / Autonomous Mining verticals

The modality pages your Physical AI stack will actually touch.

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Computer Vision
Frame-level bounding boxes, segmentation, and SAM-3-assisted annotation.
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LiDAR
3D point cloud and sensor-fusion labeling for autonomous systems.
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DICOM
Clinical imaging annotation with PHI-safe workflows, for Surgical Assistance and Hospital Operations use cases.
Blogs

From the team running your labeling ops

Guides and playbooks from the Taskmonk blog for physical AI
Ecommerce Search Relevance: How to Fix It and Measure It
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Ecommerce Search Relevance: How to Fix It and Measure It
Taskmonk Marketing
September 7, 2026
Best Appen Alternatives for Enterprise Data Labeling in 2026
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Best Appen Alternatives for Enterprise Data Labeling in 2026
Taskmonk Marketing
August 25, 2026
AI Red Teaming: What It Is and How Labeled Data Powers It
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AI Red Teaming: What It Is and How Labeled Data Powers It
Taskmonk Marketing
August 25, 2026
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FAQ

What is "Physical AI" in the context of data annotation?
Physical AI refers to AI systems like robots, autonomous vehicles, drones, and other embodied agents,  that perceive, reason, and act in the real world. Training these systems requires multimodal, sensor-synchronized datasets (image, video, LiDAR, radar, IMU, GPS, audio) rather than the single-modality data most annotation platforms are built for.
What sensor types can Taskmonk annotate for Physical AI projects?
Computer vision annotation is the process of labeling images and video, frame by frame, so machine learning models can detect, track, and classify objects. It covers bounding boxes, polygons, segmentation masks, keypoints, and 3D cuboids across formats like MP4, WebM, OGG, MKV, and HEVC.
How does Taskmonk handle multi-sensor data that needs to stay in sync?
Computer vision annotation is the process of labeling images and video, frame by frame, so machine learning models can detect, track, and classify objects. It covers bounding boxes, polygons, segmentation masks, keypoints, and 3D cuboids across formats like MP4, WebM, OGG, MKV, and HEVC.
Does Taskmonk support robotics-specific annotation needs like navigation and manipulation?
Computer vision annotation is the process of labeling images and video, frame by frame, so machine learning models can detect, track, and classify objects. It covers bounding boxes, polygons, segmentation masks, keypoints, and 3D cuboids across formats like MP4, WebM, OGG, MKV, and HEVC.
What industries is Taskmonk's Physical AI annotation used for?
Computer vision annotation is the process of labeling images and video, frame by frame, so machine learning models can detect, track, and classify objects. It covers bounding boxes, polygons, segmentation masks, keypoints, and 3D cuboids across formats like MP4, WebM, OGG, MKV, and HEVC.
How does quality control work for safety-critical Physical AI data?
Computer vision annotation is the process of labeling images and video, frame by frame, so machine learning models can detect, track, and classify objects. It covers bounding boxes, polygons, segmentation masks, keypoints, and 3D cuboids across formats like MP4, WebM, OGG, MKV, and HEVC.
Is Physical AI data annotation handled differently from standard computer vision annotation?
Computer vision annotation is the process of labeling images and video, frame by frame, so machine learning models can detect, track, and classify objects. It covers bounding boxes, polygons, segmentation masks, keypoints, and 3D cuboids across formats like MP4, WebM, OGG, MKV, and HEVC.
Where is Physical AI data hosted, and is it enterprise-compliant?
Computer vision annotation is the process of labeling images and video, frame by frame, so machine learning models can detect, track, and classify objects. It covers bounding boxes, polygons, segmentation masks, keypoints, and 3D cuboids across formats like MP4, WebM, OGG, MKV, and HEVC.

Get the Data Right Before You Teach a Robot to Act

Multimodal, sensor-synchronized, production-ready datasets for Physical AI teams.
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