Miso Motion · v1.0 · 2026

Real-world motion
intelligence for
physical AI.

Structured service-task data captured from expert human work in real environments.

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 has not existed at scale. Until now.

Data signals required

Hands & wrist motion

Tool use & grip

Object manipulation

Surface interaction

Failure & recovery

Environment variability

Task sequencing

Outcome signals

Section 02 / Product

A structured data layer for physical AI.

Miso Motion transforms real service work into model-ready training data. Not raw footage — structured, labeled, and contextualized data purpose-built for robotics and embodied AI teams.

01 / Capture

Egocentric Video

First-person recordings from head-mounted rigs during live service tasks across real homes and businesses.

02 / Annotation

Task Segmentation

Each clip is segmented into discrete tasks and subtasks with frame-level temporal boundaries and semantic tags.

03 / Interaction

Hand–Object Mapping

Precise tracking of hand positions, grasp types, and contact events with identified objects and surfaces.

04 / Context

Environment Metadata

Room type, surface materials, object categories, lighting conditions, and spatial layout per session.

05 / Labels

Tool & Surface Tags

Standardized ontology of tools, materials, and surfaces applied consistently across all task domains.

06 / Outcomes

Before / After Signals

Verified outcome states — task completion, quality assessments, and annotated failure modes.

Section 03 / Dataset

Four task domains. One data infrastructure.

Domain 01

Clean

Cleaning tasks across surfaces, environments, and tool types. High-frequency, repetitive manipulation with clear before/after outcome signals.

Surface cleaning
Tool handling
Repetitive motion patterns
Multi-room sequences

Domain 02

Move

Object handling, packing, and organization tasks. Emphasis on grasp strategies, spatial reasoning, and sequential planning under real constraints.

Object relocation
Packing & unpacking
Spatial organization
Load management

Domain 03

Fix

Repair, installation, and maintenance work. High tool diversity, procedural complexity, and rigorous outcome verification requirements.

Appliance repair
Tool-based tasks
Installation sequences
Diagnostic steps

Domain 04

Launder

Garment care and fabric handling. Deformable-object manipulation with fine-grained dexterity, ironing, folding, and sorting under varied textiles.

Ironing & pressing
Folding sequences
Sorting & pairing
Deformable handling

Section 04 / Differentiation

Why Miso has a unique right to win.

Miso already operates at the scale required to build this dataset. We don't collect from scratch — we collect from a live service network active in real homes and businesses today. That changes what is possible.

→1.5M+ households and 130K+ providers represent an unmatched source of in-home service intelligence at real scale.
→15M+ completed bookings provide outcome signal at a volume impossible to replicate synthetically or in a lab setting.
→Providers are verified professionals working in real, uncontrolled environments — not actors in staged conditions.
→Miso Motion is built on existing service infrastructure — not a data collection effort constructed from the ground up.
→Privacy and consent are embedded in the operational layer, not retrofitted after collection.

Section 05 / Specifications

Built for robotics teams.

Capture

Egocentric video from head-mounted rigs in live service environments.

Annotation

Task and subtask labels with frame-level temporal boundaries and semantic classification.

Interaction

Hand–object contact events, grasp classification, and object state transitions per frame.

Metadata

Tool type, surface material, environment class, and spatial layout descriptors per clip.

Outcomes

Before/after state verification with task completion, quality, and failure mode annotations.

Privacy

Consent-first collection. Face and PII redaction workflows applied before any distribution.

Format

Packaging aligned to robotics and embodied AI training pipelines. Custom splits on request.

Access

Research licensing and enterprise partnerships. Apply via the Miso Motion contact form.