Robotics

Intelligent annotation for robotic perception, manipulation, automation, and next-generation physical AI systems

High-fidelity annotation for robots, robotic arms, objects, human-robot interactions, workspaces, and multi-modal sensor data. Eidoryx supports AI systems that need to perceive their surroundings, understand objects and environments, plan movements, and perform precise physical tasks across industrial, warehouse, healthcare, and real-world robotic applications.

Book Demo Types of Annotation

Types of Annotation

11 annotation types for robotics data.

Object & Scene Annotation

Identifying and localizing objects, tools, equipment, people, and environmental elements around robots using bounding boxes, polygons, and segmentation masks. These datasets support robotic perception, object recognition, navigation, and manipulation.

Robot & Robotic Arm Annotation

Annotating robotic arms, joints, grippers, end-effectors, mobile robots, humanoid robots, and other robotic systems. These datasets support robot perception, pose estimation, motion analysis, and automation.

Object Manipulation Annotation

Labeling objects being picked, placed, moved, grasped, assembled, or manipulated by robots. These annotations support robotic manipulation, grasp planning, automated assembly, and learning-from-demonstration systems.

Grasp & Affordance Annotation

Annotating grasp points, contact areas, handles, surfaces, edges, and other regions where a robot can interact with an object. These datasets support robotic grasping, manipulation, and task planning.

Human-Robot Interaction Annotation

Annotating people, human poses, gestures, actions, interaction zones, and robot-human activities. These datasets support collaborative robots, service robots, assistive robotics, and safe human-robot interaction.

Robot Pose & Keypoint Annotation

Annotating robot joints, keypoints, joint positions, body configurations, and end-effector locations. These datasets support pose estimation, motion planning, robot control, and imitation-learning applications.

Robot Trajectory & Motion Annotation

Tracking robot movements, object trajectories, joint movements, and task sequences across video or sensor data. These annotations support motion prediction, trajectory learning, navigation, and autonomous task execution.

3D & Point Cloud Annotation

Annotating objects, surfaces, obstacles, environments, and spatial relationships within LiDAR, depth, RGB-D, and 3D point-cloud data. These datasets support 3D perception, spatial understanding, navigation, and robotic manipulation.

Navigation & Obstacle Annotation

Identifying obstacles, pathways, walls, doors, stairs, furniture, people, and navigable areas. These datasets support autonomous navigation, path planning, obstacle avoidance, and mobile robotics.

Task & Action Annotation

Labeling robotic tasks and action sequences such as picking, placing, sorting, opening, closing, pushing, pulling, assembling, and transporting. These datasets support imitation learning, behavior learning, and task automation.

Robotic Environment Segmentation

Pixel-level segmentation of workspaces, floors, shelves, machinery, objects, obstacles, and other environmental regions. These datasets support robotic scene understanding, navigation, manipulation, and physical AI.

Quality Assurance

Multi-stage annotation, senior review, and final verification.
Robotics-specific taxonomies covering robots, objects, actions, poses, environments, interactions, and task sequences.
Detailed review of object boundaries, grasp points, robot joints, end-effectors, and interaction regions.
Frame-to-frame consistency checks for robot movements, object manipulation, trajectories, and task sequences.
Dedicated review of occluded objects, overlapping robotic components, complex workspaces, and human-robot interactions.
3D and point-cloud quality checks for spatial relationships, object boundaries, depth, and positioning.
Verification of action and task labels against project-specific robotic workflows and guidelines.
Accuracy and completeness thresholds established before final delivery.

Why Eidoryx for Robotics?

Built for complex robotic and physical AI environments, where robots, objects, people, spatial relationships, and physical actions must be accurately understood. Eidoryx combines structured visual and sensor-data annotation with robotics-focused quality workflows to create reliable datasets for robotic perception, manipulation, autonomous navigation, industrial robotics, warehouse automation, humanoid robots, collaborative robots, imitation learning, human-robot interaction, 3D perception, and next-generation physical AI systems.

← Security & Surveillance Document AI & OCR →

AI Needs More Than Data.

Talk to our team about annotation programs, datasets, and enterprise AI infrastructure tailored to your domain.