Venture in development

Testing a data layer for robots in real workplaces.

The input is human task examples. The output is cleaner training and evaluation data for robotics teams in Asia.

Task

Human demonstration

Context

Rooms, tools, objects

Rights

Consent and usage scope

Structure

Sequences and metadata

Quality

Checks and annotation

Use

Training and evaluation

The problem

Real workplaces are hard to turn into clean training data.

Robots need to work outside labs. Real spaces are crowded, inconsistent, and full of small decisions.

Task variation

People do the same task with different tools, layouts, and recovery moves.

Human-object interaction

Useful data shows products, spaces, obstacles, and mistakes.

Collection ops

Access, consent, annotation, quality checks, and rights all matter.

The venture thesis

Robotics teams do not only need more data. They need useful demonstrations.

The thesis is simple. Teams may need varied, structured, legal task data from Asian workplaces.

I am testing whether a collection network can provide clear rights, useful metadata, and enough variation.

What could be collected

Task areas under review.

These are exploration areas, not finished services or available datasets.

Domestic and commercial cleaning
Hospitality and food-service workflows
Retail and product handling
Warehousing and fulfilment
Facilities and building operations
Human-object interaction
Repetitive manual processes
Difficult or unusual edge cases

How the model could work

From task need to structured dataset.

01

Define the task

Set the task, capture mode, metadata, and success checks.

02

Find settings

Find real workplaces with suitable layouts and participants.

03

Secure consent

Set participation terms, usage rights, and privacy rules.

04

Capture examples

Collect repeated examples through agreed capture modes.

05

Structure and check

Label task steps, outcomes, and quality issues.

06

Deliver data

Provide a defined dataset or recurring data supply.

Why Asia

Dense cities, varied work, and practical access.

The first focus is Hong Kong and nearby markets. Logistics, hospitality, retail, and facilities work create varied tasks.

Hong Kong is a useful place to test access, rights, task detail, and buyer demand.

Currently being validated

The immediate work is demand discovery.

The goal is to test demand before building supply.

Which tasks robotics companies will pay to collect
One-off datasets versus ongoing data supply
Required volume, diversity and sensor requirements
Annotation, metadata and quality expectations
Ownership, licensing and usage rights
Acceptable collection costs
Suitable partner environments
Privacy, consent and governance requirements

Who I want to speak with

Conversations that can sharpen the first pilot.

Robotics companies

Teams seeking task demonstrations, evaluation data or harder-to-source real-world scenarios.

Investors and venture builders

People interested in robotics infrastructure and data businesses.

Collection partners

Hotels, cleaners, retailers, warehouses, facility operators and other businesses with suitable workflows.

Researchers and advisers

People with experience in robotics datasets, teleoperation, computer vision, annotation, simulation or data governance.

Founder note

Developed from Hong Kong, with validation before scale.

I am developing this venture from Hong Kong. My background spans software systems, CRM, reporting, and commercial operations. The next job is to prove buyer demand and define a responsible model.

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Working on robotics data or real-world automation?

I am speaking with buyers, partners, researchers, and investors. The goal is a useful first pilot.

Start a conversation