Talk to an Expert
Talk to an Expert

Real-world data
infrastructure
for Physical AI

Human, robot and world layers — aligned, annotated, and ready to train against.

Talk to an Expert →Get sample pack →

What Abaka Delivers

Capture, align, and deliver human-to-robot experience data for Physical AI.

The Three-Layer Foundation for Physical AI: Human Layer, Robot Layer, and World Layer flowing into a Physical AI System.

The Three-Layer Foundation
for Physical AI

01 Human Layer Video Pose Tactile
02 Robot Layer UMI Teleop
03 World Layer World-Model Simulation Scene
Demonstration signal Execution context Physical AI System
videoposetactile

Human Layer

Real-world, first-person human capture — the source signal for teaching robots how people actually move, reach, and interact with objects. Aligned end-to-end, ready to train against.

UMIteleoptactile

Robot Layer

UMI capture and teleoperation data. The bridge from human demonstrations into real robot execution.

world-modelsimulationscene

World Layer

World-model data from the same pipeline. Simulation, scene and context that VLA models actually train against.

Dataset diversity

A broad spread of tasks, scenes, and countries for models that need to generalize beyond a narrow lab set.

| Dataset Coverage

Explore diversity across task type, scene, and country coverage.

Task Scene Country Task Distribution Retail 20.9% Cooking 19.1% Cleaning 16.6% Retail & Merchandising 20.9% Shelf Restocking20.6% Product Merchandising19.1% Order Packing17.7% Parcel Unpacking16.3% Barcode Scanning6.7% Fresh Produce Sorting6.2% Cash Register Handling5.3% Gift Wrapping4.3% Fitting Room Reset3.8% Cooking & Meals 19.1% Meal Preparation33.5% Dishwashing26.7% Food Plating15.7% Fridge Restocking9.9% Beverage Brewing7.9% Coffee Brewing6.3% Cleaning & Sanitation 16.6% Countertop Cleaning35.5% Room Tidying32.5% Floor Mopping15.7% Trash Emptying9.6% Waste Sorting6.6% Storage & Organization 13.5% Pantry Organization21.5% Inventory Counting20.0% Bed Making16.3% Tool Organization14.8% Material Staging14.1% Container Filling13.3% Assembly & Manufacturing 11.9% Quality Check21.0% Parts Sorting19.3% Component Assembly18.5% Fastener Installation17.6% Equipment Calibration13.4% Parts Replacement10.1% Dressing & Textile 9.2% Laundry Folding50.0% Table Setting34.8% Apparel Folding15.2% Logistics & Care 8.8% Item Labeling19.3% Box Sealing19.3% Trolley Transport17.0% Handoff Transfer14.8% Plant Care11.4% Equipment Cleaning10.2% Battery Swap8.0% Scene Distribution Home 45.0% Commercial 35.5% Factory 19.5% Home 45.0% Kitchen13.8% Living Room11.6% Bedroom11.0% Bathroom9.7% Balcony6.1% Dining Room5.4% Children's Room5.4% Laundry Room5.2% Home Office / Study5.1% Foyer / Entry5.0% Commercial 35.5% Restaurant9.5% Supermarket9.2% Business Office6.6% Shopping Mall Concourse6.4% Convenience Store5.9% Hotel Interior5.9% Coffee Shop5.5% Retail Stockroom5.4% Fresh Grocer5.2% Apparel Store4.7% Factory 19.5% Production Floor9.2% Processing Bay8.8% Packaging Line8.4% Assembly Line7.1% QC Inspection Zone6.8% Raw Material Store6.5% WIP Buffer Zone5.9% Finished Goods Store5.8% Dock / Loading Bay5.4% R&D Lab4.9% Country Distribution United States 28.7% India 20.5% Other countries 14.3% United States 28.7% Country coverage28.7% India 20.5% Country coverage20.5% Nigeria 8.0% Country coverage8.0% Kenya 7.3% Country coverage7.3% Brazil 5.6% Country coverage5.6% Canada 4.4% Country coverage4.4% United Kingdom 3.7% Country coverage3.7% Egypt 2.9% Country coverage2.9% Philippines 1.4% Country coverage1.4% Indonesia 1.3% Country coverage1.3% Argentina 1.1% Country coverage1.1% Pakistan 0.9% Country coverage0.9% Other countries 14.3% Country coverage14.3%

How it works

A clear, end-to-end process from your requirements to training-ready data.

01

Scope

We start by understanding your use case and shaping the data plan around what your model actually needs.

02

Collect

Real-world data is captured in the environments and scenarios that matter to your model's performance.

03

Annotate

Data is labeled and reviewed for consistency, so what you receive is ready to train against.

04

Deliver

Datasets are handed off in a structured, versioned format that fits your training workflow.

Why Teams
Choose
Abaka

CONSENT Consent-first Data is collected with informed consent as a foundation, not an afterthought. CONTRIBUTORS Fair to contributors The people behind the data are recognized and compensated for the work they contribute. EXCLUSIVITY Built for your use case Custom datasets are built around your needs and stay aligned with your project. RESPONSIBLE Responsible practices Privacy and data-handling considerations are built into how we plan and deliver every project.

Your Model Needs
Real-World Data

Human, robot and world signals — aligned, annotated, and ready to train against.

Talk to an Expert →Get sample pack →