The data brick to scale the next generation of embodied AI

ACTAGON

ACTAGON is built to help robots operate in complex human environments.

A real-world dataset for navigation, interaction, and action.

Real-World Data Infrastructure

A deployment-ready system for collecting structured first-person interaction data in the real world.

Our smart-glasses platform is already built. We are now focused on deploying it with real contributors to capture high-quality action data from everyday human environments.

Primary Goal
High-quality real-world data for embodied AI
Broader Impact
New earning opportunities through data contribution

Live Dataset Demo (Preview)

Upload a short POV video. We sample a few moments and return a preview JSON.

1 Upload video
2 Generate preview
3 Review frames + JSON

Upload video

Step 1

Best for short POV clips longer than 5 seconds.

Waiting for upload…

Output

Step 3

Preview only. Models can make mistakes. 

Upload a video to generate a sample JSON output…

From Language Prediction → Action Prediction

Same idea, different output: language models predict the next token, while embodied AI must predict the next physical action.

Language Models

Predict the next token

The model uses prior context plus the current token to estimate what word piece comes next.

Language model prediction example

Embodied AI

Predict the next action

The system uses prior context plus the current action to estimate what physical behavior should happen next.

Action prediction example: walking
Past context What already happened
Current state What is happening now
Next prediction Token or action

Walking

Action prediction example: walking

Cooking

Action prediction example: cooking

Industrial Repair

Action prediction example: industrial repair

Real-world first-person data is the missing layer that helps models move from understanding language to understanding behavior.

How ACTAGON Gets Built

A real-world data pipeline for embodied AI

We focus on authentic environments, scalable capture, and deployment operations that work outside the lab.

01

Human-centered data collection

Capture data from real contributors in everyday environments.

  • household workers
  • domestic helpers
  • service workers
  • everyday home environments

02

Real-world interactions at scale

Collect the messy, long-tail behaviors that labs and simulation miss.

  • natural hand-object interaction
  • real clutter and environment variability
  • diverse routines and motion styles
  • practical, everyday tasks

03

Deployment in the field

Support collection with local operations, onboarding, and quality control.

  • local field operators
  • contributor onboarding and education
  • quality control at the source
  • ongoing engagement
Smart glasses journey: prototype to MVP to product; CES showcase

Prototype → MVP → Product & CES showcase → Deployment

Execution Advantage

Hardware + deployment experience

We are not starting from zero. The platform has already moved from prototype to product-ready deployment.

Lightweight POV capture Wearable smart glasses designed for real-world use.
Scalable deployment Built to operate beyond one-off lab pilots.
Consistent data quality Structured capture workflows from the start.
Real-world usability Designed for contributors in everyday environments.

This gives us a faster path to execution than software-only approaches.

Privacy Layer

Privacy-first by design

All captured footage is processed through privacy safeguards before annotation or downstream use.

face blurring screen blurring personal identifier removal consent-based workflows

Privacy is part of the infrastructure, not an afterthought.