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Robotics Data Collection Services for AI Training

We design and execute end-to-end data collection programs for robotics AI — capturing the multimodal, precisely synchronized sensor data that robotic systems need to perceive, reason, and act in the physical world. From manipulation and navigation to human-robot interaction and industrial automation, we deliver datasets with the spatial precision, temporal alignment, and annotation depth required to train production-grade robotic models.

Get started Cases
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25+ crowdsourcing platforms
30+ industries

Our Expertise

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Manipulation & grasping data

RGB and depth footage of robotic and human hands interacting with objects — picking, placing, assembling, sorting — captured from multiple synchronized viewpoints with force-torque sensor logs and joint state recordings.

Industry use cases

  • robotic pick-and-place systems
  • bin picking
  • assembly automation
  • warehouse fulfillment robots
  • surgical instrument handling
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Navigation & mapping data

Sensor streams from robots moving through structured and unstructured environments — LiDAR scans, odometry, IMU readings, stereo camera feeds, and SLAM-generated maps — collected indoors, outdoors, and in transitional spaces.

Industry use cases

  • autonomous mobile robots (AMR)
  • last-mile delivery robots
  • inspection drones
  • agricultural navigation
  • indoor logistics automation
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Human-robot interaction (HRI) data

Recordings of humans and robots sharing physical space — handovers, collaborative tasks, proximity behavior, gesture-based commands, and verbal interaction — captured with full body pose, gaze, and speech modalities.

Industry use cases

  • collaborative robot (cobot) safety systems
  • social robotics
  • assistive robots
  • service robots in hospitality and healthcare
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Teleoperation & demonstration data

Human operator demonstrations captured via teleoperation rigs, VR controllers, or kinesthetic teaching — recording end-effector trajectories, force profiles, and operator intent for imitation learning and behavior cloning pipelines.

Industry use cases

  • imitation learning
  • behavior cloning
  • learning from demonstration (LfD)
  • dexterous manipulation research
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Whole-body & locomotion data

Motion capture and sensor recordings of legged robots, humanoids, and mobile manipulators performing locomotion tasks — walking, climbing, balancing, and recovering from perturbations — across terrain types and surface conditions.

Industry use cases

  • humanoid robot training
  • legged robot locomotion
  • exoskeleton control
  • disaster response robots
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Industrial process & inspection data

Visual and sensor data from manufacturing lines, quality control stations, pipeline inspection runs, and infrastructure monitoring — including thermal, ultrasonic, and vibration sensor streams alongside RGB video.

Industry use cases

  • robotic quality inspection
  • predictive maintenance
  • weld inspection
  • infrastructure monitoring drones
  • automated defect detection
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Egocentric & wrist-mounted data

First-person video and sensor streams captured from robot-mounted or wrist-mounted cameras, recording the robot's own perspective during manipulation and navigation tasks, often paired with proprioceptive data.

Industry use cases

  • embodied AI training
  • end-to-end visuomotor policy learning
  • contact-rich manipulation
  • dexterous hand control
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Synthetic & simulation data

Physics-engine-generated sensor streams — rendered RGB-D, simulated LiDAR, synthetic force feedback — produced in platforms like Isaac Sim or MuJoCo, with automatic ground truth labels for pose, depth, and segmentation.

Industry use cases

  • sim-to-real transfer
  • rare scenario coverage
  • large-scale pre-training
  • domain randomization pipelines
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Data Collection Methods

Teleoperation capture

Human operators control robot hardware remotely via VR controllers, space mice, or exoskeleton rigs while full sensor streams are recorded. Produces high-quality demonstration data directly usable for imitation learning and behavior cloning.

Kinesthetic teaching & physical demonstration

A human physically guides the robot through a task while joint states, end-effector pose, and force-torque data are logged. Ideal for contact-rich manipulation tasks requiring natural motion priors.

Autonomous robot operation logging

Instrumented robots execute tasks in controlled or real-world environments while all onboard sensor streams are continuously recorded, filtered, and timestamped for downstream annotation.

Human motion capture & mirroring

Human participants perform target tasks wearing MoCap suits or marker arrays. Captured motion data is mapped to robot morphology for training locomotion and manipulation policies.

Simulation & synthetic generation

Robotic scenarios are replicated in physics simulators with domain randomization across textures, lighting, object geometry, and sensor noise parameters, generating large-scale data with automatic ground truth.

Multi-modal field deployment

Sensor-equipped robots or human operators are deployed in real target environments — warehouses, hospitals, construction sites, outdoor terrain — to collect authentic in-context data at scale.

Platforms and Tools

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Capture hardware

ROS-compatible robot platforms, Intel RealSense and ZED stereo cameras, Velodyne and Ouster LiDAR, ATI force-torque sensors, OptiTrack and Vicon motion capture systems, VR teleoperation rigs (Meta Quest, HTC Vive)
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Data recording & synchronization

ROS 2 (rosbag2), MCAP format for multi-modal logging, custom hardware-triggered synchronization rigs ensuring sub-millisecond alignment across modalities
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Simulation platforms

NVIDIA Isaac Sim, MuJoCo, PyBullet, Gazebo, Genesis; domain randomization pipelines for sim-to-real transfer
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Annotation tools

CVAT (3D point cloud and video annotation), Scale AI robotics annotation, proprietary trajectory and keypoint labeling interfaces, Label Studio with custom robotics schemas
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Processing & transformation

Open3D, PCL (Point Cloud Library), ROS TF stack, custom calibration pipelines for extrinsic and intrinsic sensor alignment
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Storage & delivery

MCAP, HDF5, rosbag2, Parquet; structured episode format compatible with Open X-Embodiment, RLDS, and LeRobot dataset standards

Project Steps

01 Discovery & requirements scoping
We begin with a deep technical scoping session to understand your robot platform, task domain, sensor configuration, policy architecture, and data volume requirements. We define episode structure, annotation schema, synchronization tolerances, and compliance requirements — including any safety protocols for human participant involvement
02 Sensor rig design & calibration
Our engineers configure and calibrate the full sensor stack for your collection environment — camera intrinsics and extrinsics, LiDAR-camera alignment, IMU integration, and force-torque sensor zeroing. Calibration files and validation reports are delivered alongside the dataset
03 Environment setup & task design
We design the physical or simulated collection environment, define task protocols and success criteria, recruit and train operators or human participants, and produce detailed data collection runbooks to ensure consistency across sessions
04 Pilot collection & validation
A pilot batch of episodes is collected, synchronized, and reviewed for sensor alignment, data completeness, task execution quality, and annotation accuracy. Synchronization drift, sensor drop-out rates, and episode quality metrics are reported before full-scale production begins
05 Full-scale collection & annotation
Production collection runs across all planned tasks and environments. Annotation teams apply trajectory labels, object pose annotations, action segmentation, success flags, and any custom task-specific labels — with continuous quality monitoring throughout
06 Quality assurance
Every episode is validated for temporal synchronization integrity, sensor stream completeness, annotation accuracy, and task success labeling. Automated checks run alongside expert human review, with failed or ambiguous episodes flagged for re-collection or adjudication
07 Delivery & ongoing partnership
Datasets are packaged in your target format — compatible with Open X-Embodiment, RLDS, LeRobot, or a custom schema — and delivered via secure cloud transfer. We support iterative dataset expansion for new tasks, targeted gap-filling for underrepresented scenarios, and long-term data partnerships as your robotic system scales

Frequently Asked Questions

Can you annotate the robotics data you collect?
Yes. Robotics data collection, validation, and annotation can be handled in one workflow, with consistent technical and metadata requirements. Our 1,100+ labelers and specialists annotate images, video, LiDAR, point clouds, and sensor data based on the robotic system and machine learning model, with defined labeling guidelines and quality controls.
Do you offer fully customized robotics datasets?
Yes. Robotics datasets can be designed around the specific requirements of the robotic platform, environment, task, and AI model. Customization can include robot type, sensors, cameras, locations, scenarios, operating conditions, object classes, human interactions, data volume, annotation requirements, metadata, and quality thresholds.
How much data is needed to train a model?
There is no universal dataset size for robotics training. The required volume depends on the robotic task, model architecture, pretraining, environment diversity, sensor configuration, object and scene variation, number of edge cases, and target performance. We recommend beginning with a representative pilot, evaluating model performance and identifying gaps through error analysis, and then expanding the collection based on the scenarios and conditions.
How long does a robotics data collection project take?
Project duration depends on the data volume, robotic platform, sensor setup, number of scenarios, collection locations, environmental requirements, equipment, annotation complexity, validation procedures, and client review cycles. After assessing feasibility, we can establish a project plan covering the pilot, equipment and site preparation, collection ramp-up, expected production capacity, quality checks, annotation, and final delivery.
How do you ensure data security?
Security controls are defined for each project based on the robotics data type, collection environment, and client requirements. Depending on the scope, they may include NDAs, role-based access, secure data transfer and storage, data minimization, and agreed retention or deletion rules. Our data collection processes support privacy compliance based on the project scope, sensor data, collection environment, and jurisdictions involved.
What data and sensor formats can you deliver?
We can deliver robotics datasets in standard or project-specific formats based on the requirements of the client's robotic systems and training pipeline.

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    We partnered with Unidata for a vast collection of webcam and smartphone assets and were very satisfied. The data quality was excellent, maintaining high resolution and clarity across all formats to meet our specific technical needs. The procurement process was seamless thanks to their customer service. The team was proactive and transparent, ensuring smooth communication from start to finish. Although the investment was significant, the professional caliber of the data and the reliability of the service made it a worthwhile value.

    D. B. IdentifAI Labs

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