This blog explores why multilingual data annotation is uniquely challenging, outlines the key dimensions that define its quality and...
Read MoreHuman-in-the-Loop ML Data Annotation Services
Human-in-the-loop AI data annotation services for the sensor data that powers robots, autonomous vehicles, drones, and smart machines.
Why Annotation Is Different for Physical AI
Physical AI systems don’t just classify pixels or tokens; they operate in 3D space, in real time, around people and infrastructure. That means their training data must reflect:
Multi-sensor inputs (multi-camera rigs, LiDAR, radar, GPS/IMU)
Complex, dynamic scenes (traffic, factories, warehouses, farms)
Safety-critical edge cases (near-misses, occlusions, rare events)
DDD’s AI data annotation services are designed for Physical AI, combining specialized workflows, domain-trained teams, and rigorous QA to deliver trustworthy labels for perception, mapping, navigation, manipulation, and human–robot interaction.
What We Annotate for Physical AI
Perception Data (Vision, LiDAR, Multimodal)
Use for: object detection, tracking, collision avoidance, perception, and scene understanding.
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- 2D Vision: Bounding boxes, polygons, and masks; instance & semantic segmentation; multi-frame tracking.
- LiDAR / 3D: Cuboids for agents and static objects; drivable vs. non-drivable space; infrastructure (poles, rails, docks, racks).
- Sensor Fusion: Consistent labels across camera–LiDAR projections and BEV views.
Supports AV/ADAS, mobile robots, drones, and inspection robots.
Mapping & Localization Annotations
Use for: HD mapping, SLAM, localization, route planning, and environment modeling.
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- Road & Outdoor: Lanes, crosswalks, stop lines, traffic lights, signs, and barriers.
- Indoor: Aisles, docking zones, shelves/racks, markers, waypoints, and no-go zones.
- Structures: Walls, doors, stairs, loading bays, mezzanines, and SLAM landmarks.
Manipulation & Task-Level Labels
Use for: grasping, pick-and-place, assembly, task learning, and cobots.
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- Manipulation Annotations: Grasp points, affordances, contact surfaces, and keypoints on tools and parts.
- Task Phases: Approach, grasp, move, place, and verify, with success/failure states and error tags.
Human–Robot Interaction & Safety Labels
Use for: HRI, collaborative robots, safety compliance, social navigation.
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- Human Pose & Safety: Human pose/keypoints, proximity zones, and dynamic safety envelopes.
- Gestures & Intent: Hand signals and direction of movement.
- Scenario Tags: Near-collision, obstruction, queueing, handover, and co-manipulation.
Telemetry, Logs & Event Annotation
Use for: reinforcement learning, diagnostics, and post-deployment analysis
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- Robot State & Control Logs: Operating modes, control commands, and error states.
- Sensor Health & Diagnostics: Sensor status, faults, and diagnostic events.
- Environment Events: Door states, machine statuses, and alarms.
- Time-Aligned Episodes: Structured episodes for RL training, evaluation, and edge-case mining.
Industries We Serve
ADAS
High-precision annotations for lanes, signs, and obstacles to improve ADAS perception and safety.
Autonomous Driving
Multi-sensor annotations enabling accurate perception and decision-making from L2+ assistance to full autonomy.
Robotics
Reliable labeling for navigation, obstacle detection, and manipulation tasks across industrial and service robots.
Healthcare
Clinical-grade annotations for medical imaging, diagnostics, and patient monitoring to enhance healthcare AI accuracy.
What Our Clients Say
Why Choose DDD?
Domain-Trained Physical AI Teams
Annotators and leads are trained on robotics and autonomy concepts (ODD, sensor stacks, coordinate frames, safety envelopes) so they understand why labels matter, not just where to click.
Multi-Sensor & 3D Native
We’re comfortable in 3D tooling, bird’s-eye views, and multi-camera rigs, not just single 2D images, crucial for AV, drones, and warehouse robots.
Human-in-the-Loop at Scale
You get production-grade teams and QA that can grow from pilot spans of a few sequences to full-fleet, continuous annotation, without sacrificing quality.
Tool & Platform Agnostic
We integrate with your existing Physical AI toolchain: commercial 3D/AV labeling platforms, in-house data tools, or GIS environments, no forced platform migration.
Quality, Safety & Governance
Our quality and safety are strengthened through maker–checker workflows, gold tasks, targeted quality metrics, and clear escalation paths.
Read Our Latest Blogs
Explore the latest techniques and thought leadership shaping the future of ML data annotations.
Multi-Modal Data Annotation for Autonomous Perception: Synchronizing LiDAR, RADAR, and Camera Inputs
This blog explores multi-modal data annotation for autonomy, focusing on the synchronization of LiDAR, RADAR, and camera inputs. Practical...
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The Art of Data Annotation in Machine Learning
Data Annotation has become a cornerstone in the development of AI and ML models. In this blog, we will...
Read MoreCustomer Success Stories
See how DDD accelerates physical AI innovation through data-driven success stories.
Accelerating ADAS Model Development through 2D and 3D Annotations
A leading autonomous vehicle manufacturer sought to enhance the safety and accuracy of its Advanced Driver Assistance Systems (ADAS).
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Object detection in LIDAR with 98% quality consistency
Although one of the more straightforward LiDAR data processing tasks, object boxing is still challenging. For applications like ADAS, the task requires extreme precision.
Explore solutions →
Ready to Train Physical AI That Works in the Real World?
Frequently Asked Questions
What is ML data annotation, and why is it important?
ML data annotation is the process of labeling images, videos, LiDAR, text, or audio so AI models can learn patterns. Accurate annotations directly improve model precision, safety, and real-world reliability.
How does DDD ensure annotation accuracy?
Through multi-layer QA workflows, expert-led reviews, domain-specific training, performance auditing, and real-time quality dashboards.
Can DDD scale for millions of annotations?
Yes. Our global workforce and productionized workflows are built for enterprise-scale annotation across complex datasets.
Does DDD support secure and compliant data handling?
Absolutely. DDD maintains strict data governance with secure access controls, compliance with industry standards, and dedicated secure annotation environments.