need to act.
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Built for robotics teams that need data from real environments
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Robots do not learn from clean theory. They learn from messy reality.
Physical AI systems fail when training data does not reflect real-world variation. Lighting changes. Objects move. Humans adapt. Hands block cameras. Edge cases appear after deployment. dexset captures the data robots actually need to improve.
From real-world task capture to ML-ready datasets.
Define the Task
Clarify the task, environment, objects, capture angles, success criteria, and dataset requirements.
Capture Real-World Data
Collect egocentric, exocentric, multi-view, sensor, and teleoperation data across relevant environments.
Annotate Interactions
Label objects, actions, poses, failures, sequences, grasp points, scenes, and task outcomes.
Validate Quality
Score datasets for completeness, consistency, coverage, variation, and model-readiness.
Deliver to Pipeline
Export data in formats compatible with computer vision, robotics, and ML workflows.
Everything needed to build robotics training datasets.
Real-World Video Collection
Capture task-specific video data from commercial, industrial, residential, and controlled environments.
Egocentric Data Capture
Collect first-person task demonstrations using wearable, wrist-mounted, or operator-view capture setups.
Multi-View Capture
Record synchronized exocentric and egocentric views for better spatial and contextual understanding.
Human-Object Interaction Datasets
Capture hands, tools, objects, surfaces, sequences, and outcomes across real workflows.
Dataset Annotation
Label objects, actions, poses, bounding boxes, segmentation masks, keyframes, events, and task states.
Multi-Format Delivery
Deliver datasets in COCO, YOLO, Pascal VOC, JSON, CSV, or custom schemas based on ML pipeline needs.
Datasets for the tasks robots struggle with most.
Manipulation Data
Pick, place, grasp, rotate, open, close, stack, sort, plug, fold, and assemble.
Dexterity Data
Fine motor tasks involving hands, tools, cables, packages, small objects, and irregular surfaces.
Warehouse Task Data
Picking, packing, stocking, scanning, sorting, pallet movement, bin interaction, and inventory handling.
Navigation Data
Scene understanding, obstacle interaction, route context, spatial layouts, and movement patterns.
Human-Object Interaction
How people use objects in real environments, with variation across grip, posture, sequence, and intent.
Failure Case Data
The scenarios where robots break down: occlusions, misgrips, slips, poor lighting, clutter, ambiguity.
Teleoperation Data
Data from remote or on-site robot operation to support continuous learning and deployment.
Multi-Modal Data
Video, image, metadata, sensor streams, task labels, and environment context in structured datasets.
Designed for the physical AI use cases moving fastest.
Humanoid Robotics
Task demonstrations for general-purpose robots learning everyday manipulation, navigation, and human-assistance workflows.
Warehouse & Logistics
Datasets for picking, packing, sorting, scanning, shelving, and movement across logistics environments.
Industrial Automation
Workflow data for inspection, assembly, quality checks, machine interaction, and safety monitoring.
Service Robotics
Human-assistance, hospitality, cleaning, delivery, and indoor navigation datasets.
Autonomous Vehicles
Scene understanding, object detection, edge-case capture, and environment-specific perception datasets.
Agriculture Robotics
Crop monitoring, field navigation, object recognition, harvesting support, and environment variation datasets.
Healthcare Robotics
Controlled task data for assistive robotics, device interaction, mobility support, and clinical environment workflows.
Retail Robotics
Shelf scanning, product recognition, inventory checks, restocking workflows, and store navigation datasets.
Training data is only useful when it is consistent, traceable, and complete.
Coverage
Capture enough variation across objects, environments, people, lighting, camera angles, and task sequences.
Precision
Use clear labels, consistent annotation standards, and task-specific metadata.
Traceability
Maintain source, capture, task, environment, and annotation records for every dataset.
Delivery Readiness
Format datasets for direct use in model training, evaluation, simulation, and deployment pipelines.
Why robotics teams use dexset
Real-world data, not lab-only capture
Capture the messy physical variation robots face after deployment.
Built for robot learning workflows
Structure datasets around tasks, objects, actions, environments, and outcomes.
Custom capture for specific use cases
Define the exact data your model needs instead of settling for generic datasets.
Annotation that understands physical tasks
Label interactions, sequences, poses, failures, and outcomes with task context.
Data that fits your ML pipeline
Deliver datasets in preferred formats, schemas, and structures.
Continuous improvement through teleoperations
Support post-deployment learning by capturing new edge cases and operator data.
Compliance & Trust
Compliant by design, across every dataset.
From first consent form to final delivery, dexset workflows are built to meet data protection, security, and labor standards across the regions where we capture and the regions where our customers operate.
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Need real-world data for your robotics model?
Tell us what your robot needs to learn. dexset can help define, capture, annotate, validate, and deliver the dataset behind it.
Real-world data infrastructure for robots that need to understand, move, and act.