DEXTEROUS MANIPULATION

DATA ANNOTATION

Dexterous manipulation is where robotics meets its hardest annotation problems — 6-DoF pose estimation, grasp taxonomy labeling, multi-view RGB-D synchronization, and real-world contact physics. iMerit delivers the domain-trained workforce and Ango Hub platform to get it right.
Dexterous Manipulation

ANGO HUB

MULTI-MODAL ANNOTATION PLATFORM FOR ROBOTICS

Most annotation vendors approach robotics data as a 2D image problem with extra steps. Dexterous manipulation requires a fundamentally different approach: annotators who understand grasp taxonomies, can reason about contact geometry, and can maintain label consistency across multi-view RGB-D sequences where object occlusion is constant.

SYNCHRONIZED MULTI-SENSOR ANNOTATION

RGB-D, LiDAR, IMU, force/torque, and egocentric video annotated in a unified timeline — cross-modal consistency enforced at the frame level.

TEMPORAL
INTERPOLATION

Automatic object tracking across sequential frames with annotator-controlled correction — reduces redundant re-annotation while maintaining per-frame accuracy.

AI-ASSISTED
PRE-LABELING

ML-generated initial annotations for pose and bounding box tasks, reviewed and corrected by domain-trained human annotators before delivery.

AUTOMATED
QA RULES

Configurable validation checks detect missing labels, misaligned poses, and cross-sensor inconsistencies before data exits the annotation pipeline.

ANNOTATION CAPABILITIES

Every data type your manipulation policy needs to learn from.

HAND & FINGER KEYPOINT ANNOTATION

HAND & FINGER KEYPOINT ANNOTATION

Full 21-joint hand skeleton annotation across RGB-D and egocentric video sequences. MANO parameter extraction, per-joint angle labeling, and temporal consistency across occlusion events.
OBJECT 6-DOF POSE ESTIMATION

OBJECT 6-DOF POSE ESTIMATION

Precise translation (x,y,z) and rotation (quaternion) labeling for manipulation objects across multi-view setups. Ground truth for pose estimation models used in grasp planning pipelines.
GRASP TAXONOMY LABELING

GRASP TAXONOMY LABELING

Classification across 21 grasp types — power grasps, precision pinches, tripod grips, in-hand reorientations, and functional grasps — aligned to established Feix taxonomy standards.
CONTACT REGION ANNOTATION

CONTACT REGION ANNOTATION

Per-finger, per-frame contact region masking identifying which fingers are in contact and at which object surface points — critical for contact-rich manipulation policy training.
ACTION PHASE SEGMENTATION

ACTION PHASE SEGMENTATION

Temporal segmentation of manipulation sequences into phases: approach, pre-grasp, grasp, lift, transport, place, and release — enabling models to learn task decomposition and failure recovery.
EGOCENTRIC VIDEO ANNOTATION

EGOCENTRIC VIDEO ANNOTATION

Frame-level annotation of first-person manipulation video from wrist-mounted and head-mounted cameras — including hand-object interaction labeling, contact event tagging, and gaze-aligned action segmentation across teleoperation and demonstration datasets.
“High-quality real-world data drives embodied intelligence, and consistent labeling is equally vital.”
– Head of Imaging, Humanoid Robotics Startup

CASE STUDY

Human-Centered Robot Training

Robotics startup partnered with iMerit to record, annotate, and classify real-world household task data to train next-generation humanoid robots.

Robotics startup partnered with iMerit to record, annotate, and classify real-world household task data to train next-generation humanoid robots.

The client sought a partner to coordinate 200 hours of in-home task recording using Meta Quest 3 head-mounted cameras worn by participants completing daily activities. The raw footage needed extensive structure: classification of 9 core household task types, 37 sub-classifications, and precise tracking of objects, motions, outcomes, and contextual cues.

0 hrs

Recorded household task footage

0

Task categories

0

taxonomy Subcategory
of task details

Cruise
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orbital-insight
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Use Cases

BUILT FOR THE DEXTEROUS MANIPULATION PIPELINE

Every program maps directly to the data challenges your manipulation team is actually solving — from teleoperation datasets to contact-rich policy training.

HUMANOID ROBOT TELEOPERATION DATASET

iMerit coordinates human operators performing household and industrial manipulation tasks, captured from wrist and head-mounted cameras. Annotated with hand keypoints, action phases, and contact events across diverse object categories.

GRASP POLICY TRAINING FOR BIN PICKING

6-DoF pose estimation and grasp taxonomy labeling across hundreds of object categories in cluttered bin environments. The most common high-volume dexterous manipulation annotation program in production — covering power grasps, precision pinches, and functional grasps.

CONTACT-RICH MANIPULATION FOR IN-HAND REORIENTATION

Per-finger contact region masking and force/torque event labeling for manipulation sequences involving in-hand object reorientation. The hardest annotation problem in the dexterous manipulation stack — and the most differentiated capability iMerit offers.

VLA MODEL TRAINING DATA PIPELINE

End-to-end annotation pipeline for Vision-Language-Action model training — action phase segmentation, object state labeling, and instruction-action alignment across large-scale demonstration datasets. Directly relevant to foundation model development for physical AI.

INDUSTRY VERTICALS

Built for every domain where robots need to understand and interact with the physical world.
INDUSTRIAL-HUMANOID-ROBOTS

INDUSTRIAL
HUMANOID ROBOTS

Grasp taxonomy, object pose estimation, action phase segmentation, and multi-sensor fusion annotation for humanoid manipulation programs.

WAREHOUS-LOGISTICS

WAREHOUSE &
LOGISTICS

Pick-and-place sequence labeling, bin-picking pose annotation, conveyor tracking, and pallet interaction labeling for AMR and robotic arm programs.

SEMICONDUCTOR--ELECTRONICS

SEMICONDUCTOR &
ELECTRONICS

Precision manipulation annotation for high tolerance PCB assembly, chip handling, and micro component placement — supporting cobot programs where millimeter-level accuracy is required.

AGRICULTURAL-ROBOTICS

AGRICULTURAL
ROBOTICS

Crop interaction annotation, grasp labeling for irregular organic shapes, plant detection, and multi-sensor fusion for precision harvesting systems.

MANUFACTURING--ASSEMBLY

MANUFACTURING
& ASSEMBLY

Component identification, tool-use annotation, assembly phase labeling, and workspace segmentation for collaborative robot (cobot) programs.

HOME-&-SERVICE-ROBOTS

HOME &
SERVICE ROBOTS

Object interaction, indoor scene segmentation, intent prediction, and safe multi-agent navigation annotation for home robot and assistive AI programs.

RESEARCH-FOUNDATION-MODELS

RESEARCH &
FOUNDATION MODELS

Large-scale dataset creation aligned to established grasp taxonomies for robot learning, sim-to-real transfer, and vision language-action model training.

AUTONOMOUS-GROUND-DELIVERY

AUTONOMOUS
GROUND DELIVERY

Last-mile interaction labeling, curbside object detection, handoff gesture annotation, and dynamic environment segmentation for delivery robot programs.

WORKFORCE & QUALITY

DOMAIN EXPERTS, NOT CROWD WORKERS

The difference between 95% and 99% acceptance rate isn’t tooling. It’s whether your annotators understand what they’re labeling. iMerit’s annotators are full-time, salaried employees trained in domain-specific curricula before touching any client project.

FULL-TIME SALARIED TEAM

Not gig workers. iMerit’s annotators are permanent employees assessed at an 80%+ threshold before going live — and domain-trained before touching your data.

2-STAGE QA WORKFLOW

Every program runs a dedicated production stage followed by a separate QA review layer. Errors caught before they reach your training pipeline.

STRUCTURED PILOT FIRST

Schema design, team selection, training, and a calibration batch — all before production scale. Quality validated against your acceptance criteria.

ENTERPRISE SECURITY

SOC 2 Type II · ISO 27001 · GDPR compliant. Full audit trails and strict access controls across every program.

WHY WORK WITH US 

MANAGED GLOBAL WORKFORCE

Managed service that allows you to leverage a large and diverse global workforce to create the real-world data needed for physical AI and dexterous manipulation programs.

END-TO-END SOLUTION

From talent recruitment to data creation, collection, and annotation — iMerit combines the technology and talent into a single solution so you can focus on model development.

QUALITY ASSURED

Rigorous training and quality assurance processes with proven annotation protocols — ensuring you’re getting the high-quality dexterous manipulation data your models require.

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3D PCT DEMO VIDEO

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READY TO TRAIN

ROBOTS THAT HANDLE THE HARD CASES?

Share your sensor configuration, annotation scope, and scale targets. We’ll scope a pilot program that proves quality before you commit to production volumes.