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.
- 25+ crowdsourcing platforms
- 30+ industries
Our Expertise
Industry use cases
- robotic pick-and-place systems
- bin picking
- assembly automation
- warehouse fulfillment robots
- surgical instrument handling
Industry use cases
- autonomous mobile robots (AMR)
- last-mile delivery robots
- inspection drones
- agricultural navigation
- indoor logistics automation
Industry use cases
- collaborative robot (cobot) safety systems
- social robotics
- assistive robots
- service robots in hospitality and healthcare
Industry use cases
- imitation learning
- behavior cloning
- learning from demonstration (LfD)
- dexterous manipulation research
Industry use cases
- humanoid robot training
- legged robot locomotion
- exoskeleton control
- disaster response robots
Industry use cases
- robotic quality inspection
- predictive maintenance
- weld inspection
- infrastructure monitoring drones
- automated defect detection
Industry use cases
- embodied AI training
- end-to-end visuomotor policy learning
- contact-rich manipulation
- dexterous hand control
Industry use cases
- sim-to-real transfer
- rare scenario coverage
- large-scale pre-training
- domain randomization pipelines
Data Collection Methods
Platforms and Tools
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)
Data recording & synchronization
ROS 2 (rosbag2), MCAP format for multi-modal logging, custom hardware-triggered synchronization rigs ensuring sub-millisecond alignment across modalities
Simulation platforms
NVIDIA Isaac Sim, MuJoCo, PyBullet, Gazebo, Genesis; domain randomization pipelines for sim-to-real transfer
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
Processing & transformation
Open3D, PCL (Point Cloud Library), ROS TF stack, custom calibration pipelines for extrinsic and intrinsic sensor alignment
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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- Head of Client Success
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