Nexdata Embodied AI Data Service Framework

Nexdata has established an integrated data service framework covering off-the-shelf datasets, real-world data collection, and high-quality data annotation. This framework is built to support embodied intelligence model training, enables the development and optimization of perception, decision-making, and control capabilities of our clients.

Off-the-shelf Datasets

Over 300 million environment, decision, and control datasets are available, covering diverse scenarios, tasks, and device types, have been validated by leading industry clients.

Details

Data Collection

Real-world data collection is conducted across multiple robots, environments, and task types, ensuring authenticity and close alignment with practical application scenarios.

Data Annotation

Structured annotation services span the full perception-to-decision pipeline, supporting embodied intelligence model training, validation, and performance evaluation.

Model Validation

Comprehensive Physical AI model validation covering algorithm deployment, scenario setup, robot-based validation and feedback to accelerate model optimization and deployment.

Key Feature

Leveraging large-scale device resources and diverse real-world environments, Nexdata delivers a systematic data collection framework tailored for embodied intelligence applications.

Coverage of 300+ dexterous robotic arms, 200+ humanoid robots
Includes multiple forms such as humanoid robots, upper-body robots, robotic arms, and dexterous hands
Supports various control methods and task configurations

Coverage across indoor and outdoor application scenarios
Includes residential, commercial, warehouse, and industrial environments
Customizable scenario setup and scalable deployment based on project requirements

Supports a variety of embodied intelligence tasks, including:

Object recognition and grasping
Tool usage and manipulation
Navigation and obstacle avoidance
Human-robot interaction
Collaborative tasks (multi-person/multi-robot collaboration)

Tailored Services for Embodied AI

Nexdata provide accurate and comprehensive data support for various embodied intelligent applications. Whether it is visual perception, motion control, or interaction with the environment, we can provide the most appropriate data.

Ego-Exo Action Video Annotation

Collects aligned egocentric and exocentric recordings with fine-grained temporal segmentation, enhancing downstream training for manipulation, navigation, planning.

Motion Trajectory & Action Tracking Data

Record human or robot motion trajectories with keypoint and path annotations for action understanding and prediction.

Sim-to-Real Alignment Data

Generates simulated environment data aligned with real-world sensor data to enable cross-domain training and model generalization.

3D Scene Reconstruction Data

Provides high-precision scene scanning and point cloud annotation to support spatial reconstruction and robot navigation training.

Multi-View 3D Reconstruction Data

Reconstruction and annotate object and human body models through multi-view imaging or 3D scanning to enable high-fidelity model generation and pose recognition.

Voice-Guided Interaction Data

Provide paired datasets of voice commands and robot responses to support voice navigation and multimodal interaction scenarios.

Tactile & Force Feedback Data

Collects data from gloves and robotic arms with tactile sensors, annotating force parameters to enhance physical interaction such as grasping and manipulation..

Compliance & Security

Nexdata place the utmost emphasis on data security and client trust. We follows Personal Information Protection Act, GDPR, CCPA, PIPC and HIPAA regulations. we have also achieved ISO 27001,ISO 27701 and ISO 9001 qualifications for security and regulatory compliance. Nexdata delivers unparalleled data security, earning the trust of our clients through our adherence to these globally recognized standards.

Case Studies

Ego-Centric Data Collection for Robot Learning

USE CASE：Ego-Centric Data Collection for Robot Learning
CHALLENGE：Collecting large-scale first-person perspective data for embodied AI models across diverse home environments, requiring accurate capture of observation paths, hand movements, object interactions, and real-world task execution.
SOLUTION：Organized multi-scenario residential environments and trained dedicated data collectors using Pico devices to capture natural human activities, including object manipulation, organization, and household tasks. Delivered standardized, high-quality ego-centric data with scalable collection capacity of 5,000 effective hours per week.

UMI Data Collection for Household Robot Learning

USE CASE：UMI Data Collection for Household Robot Learning
CHALLENGE：Collecting natural household task data for robot learning across diverse home environments, requiring continuous human demonstrations and multi-scenario manipulation data covering folding, organizing, cleaning, and object handling tasks.
SOLUTION：Deployed dedicated collection teams to capture authentic household task sequences across bedrooms, kitchens, living rooms, and bathrooms. Delivered high-quality UMI data with standardized execution and quality control, providing 10,000 hours of effective data to support robot learning and Physical AI model training.

USE CASE：Humanoid Robot Task Data Collection
CHALLENGE：Data collection for humanoid robots performing short and long-range tasks (grabbing, moving, packing) across home and industrial environments, requiring synchronized visual, depth, and force data capture.
SOLUTION：Deployed dedicated engineer teams for each robot, completed full-scenario task annotation covering home and industrial settings. Output multi-modal data (video, depth, force) to support algorithm optimization, achieving 200 hours per robot per month.

USE CASE：Multi-dimensional Behavior Annotation for Embodied AI Robots
CHALLENGE：Client requires comprehensive evaluation of robot task execution through video data analysis, involving TRUE/FALSE annotation of task understanding accuracy and multi-criteria scoring across three robot types.
SOLUTION：Nexdata implemented a structured annotation framework for robotic behavior, integrating goal-state definition, temporal action validation, and multi-factor scoring across completion, feasibility, and efficiency. The framework processed dialogues, BDDL scene descriptions, action traces, and execution videos, ensuring consistent benchmarking across robot platforms to enhance task reliability and embodied AI performance.

Fine-grained Action Data Collection & Annotation

USE CASE：Fine-grained Action Data Collection & Annotation for Robotic Manipulation Tasks
CHALLENGE：Client needed high-quality datasets capturing precise robotic arm movements for complex tasks (coffee making, blackboard erasing, pen capping). Key challenges included synchronizing bi-manual motions, segmenting continuous actions into multiple sub-steps, and handling occluded or ambiguous movements.
SOLUTION：Nexdata built a structured annotation framework with temporal labeling, bi-manual task separation, and inference for occluded motions.Extended with AI-assisted tools improved boundary detection and reduced 30% manual effort, while 3-tier QC ensured 99.2% accuracy—enabling high-fidelity training data that boosted robotic manipulation precision by 40%.

Why Nexadata