Real-world data for physical AI

Real-world human demonstration data
from inside the home.

Miso Motion captures consented, first-person video of skilled service professionals performing physical tasks in real homes.

We work with robotics and physical AI teams to develop targeted data pilots around their model and evaluation needs.

View Sample Data Discuss a Pilot

Section 01 / The Challenge

Physical AI needs real-world human data.

Robotics and embodied AI systems cannot learn everything from internet video or simulation alone. The physical world is varied, unpredictable, and contextual — models need to observe real hands, real tools, real environments, and real outcomes including failure and recovery. That data is hard to stage, and harder to stage at scale.

Signals models need

Hands & wrist motion

Tool use & grip

Object manipulation

Surface interaction

Failure & recovery

Environment variability

Task sequencing

Real outcomes

Section 02 / Approach

Access to physical work that is difficult to stage.

Robotics teams can reproduce a room. Reproducing thousands of naturally different homes, objects, layouts and task conditions is harder.

Miso already coordinates skilled service work across a large residential network in South Korea. Miso Motion is building the capture, consent and quality-control systems needed to turn that access into commercially usable training data.

Our initial focus is egocentric video of everyday physical tasks, including cleaning, organizing, appliance interaction, moving and repair-related work.

Section 03 / Data

Initial data domains.

Domain 01

Cleaning

Multi-step surface cleaning, tool use and object interaction in real residential environments.

Domain 02

Organizing

Sorting, moving and placing household objects under natural spatial constraints.

Domain 03

Appliances

Opening, operating, cleaning and handling common household appliances and components.

Domain 04

Moving & repair

Packing, carrying, installation and tool-assisted service tasks.

Available footage and metadata vary by pilot. Capture setup, task scope, segmentation and annotation requirements are defined with each partner.

Section 04 / Differentiation

Why Miso is positioned to do this.

Miso already coordinates skilled service work across a large residential network in South Korea. Miso Motion is building the systems to turn that access into commercially usable training data — starting from real work already happening, not a collection effort built from the ground up.

→1.5M+ households and 130K+ providers give access to naturally varied homes, objects, layouts and task conditions — diversity that is difficult to stage.
→Miso's existing service network provides environmental diversity, skilled task execution and collection continuity that are difficult to reproduce through staged collection alone.
→Every task is real, paid work performed by a professional service provider — not crowdsourced clips or staged actors. Consistent, expert technique gives imitation-learning models a cleaner signal.
→Consent and privacy are handled at the point of collection, built into how the work is coordinated rather than retrofitted afterward.

Section 05 / Pilot

Start with a targeted pilot.

Most engagements begin with a limited, well-scoped collection designed around a specific model gap — so you can judge whether the data is useful before expanding.

01 / Gap

Define the model gap

You identify the task, environment or failure mode your model needs to cover.

02 / Design

Design the collection

We agree on capture hardware, task instructions, metadata and acceptance criteria.

03 / Validate

Validate the sample

We collect a limited dataset and determine whether it is useful before expanding.

Get Started

Inspect the data, or scope a pilot.

See representative sample data and capture specifications, or tell us about the model gap you need to cover.