Structured Data for Better Physical Policy Models

Domain-specific human input used to capture and structure data
 in real world environments
Scale Robotics Data Pipeline

Our Approach

One pipeline that takes robotics data from capture to model-ready,
with human judgment at every stage.
Expert demonstrations
Skilled experts perform real tasks on camera, captured first-person and third-person across varied environments and hardware setups.
Curated for Validity
Turn raw capture into model-ready training data with labeling, tracking, 2D and 3D annotation, QC, and evaluation.
Graded by Difficulty
Run robots in controlled environments, capture interaction data, post-process outputs, and measure performance from end to end.

Use cases

High-quality human data powering robotics systems across home, commercial, and industrial environments.
Annotations

Action-level manipulation annotations

Dense labels on robotics video, including temporal segmentation, hand and object interactions, and step-by-step natural language descriptions, built for training and evaluating vision-language-action models.
Multimodal Data

Multimodal capture pipelines

Synchronized stereo video, IMU, and multi-camera data for manipulation, motion understanding, and embodied AI research, captured to stay cleanly in sync.
Demonstrations

Human demonstration pipelines

High-quality human demonstrations across household, industrial, and task-oriented settings, collected to support manipulation learning at the scale your training needs.

Samples

Explore real-world robotics data captured across diverse environments for training, benchmarking, and embodied AI research.
Duvet folding
Large soft-object handling and alignment on a flat surface.
Pillow cover
Dressing a pillow with coordinated two-hand manipulation.
Ironing
Tool use with heated appliance and fabric repositioning.
Stitching
Fine motor control with needle, thread, and held material.
Car washing
Outdoor cleaning with tools, water, and extended reach.
Terrace cleaning
Sweeping and surface cleaning in an outdoor workspace.
Terrace water sweeping
Wet surface clearing with broom and water interaction.
Phone cleaning
Small-object handling and careful surface wiping.
iPad screen cleaning
Screen cleaning with cloth and controlled contact pressure.
Charge plug / unplug
Connector alignment, insertion, and removal with one hand.
Dishwashing
Hand manipulation of dishes, utensils, and sink-side sequences.
Towel folding
Folding and stacking fabric with consistent hand motion.

Have More Questions?

Here are some of the frequently asked questions from our customers
How do you keep quality consistent across a large dataset?

Every dataset goes through multi-layer human review: capture, quality control, and a final check before delivery. The process is built to hold quality steady from the first sample to the last.

How is data captured?

Through our expert network, in real environments, using first-person and third-person capture. For multimodal work we sync stereo video, IMU, and multi-camera streams.

How do you handle revisions?

If something needs another pass, we flag and rework it. We agree on how edge cases are handled during scoping so the final dataset matches your spec.

How much data can you collect?

We scale to the project through our network. Volume, domains, and modalities are set during scoping.

How is my data handled?

Confidential, with anonymized workflows and enterprise-grade security.

Can you help beyond robotics data?

Yes. Careerflow's Human Data team handles evaluation, annotation, and labeling across robotics, browser agents, LLMs, and a wide range of domains.

Build your robotics dataset with us

Book a call and we'll scope your data needs, from capture to model-ready delivery.