FIG. RHOS-001 / PHYSICAL AI OPERATING STACK

RHOS

Robot · Human · Object · Scene

Building next-generation physical large models from embodied data, causal world models, and robot deployment feedback.

Robotic arm in a physical AI lab
Signal Vision · Tactile · Force
Loop Collect · Model · Deploy
Target General Robot Policy
System Data + Model + Robot Flywheel
01 / Operating Thesis

Teaching physical intelligence through interaction.

Driven by the first-principle conviction that Embodied Intelligence is the essential path to AGI, RHOS studies the intersection of digital intelligence and physical reality.

Our RoboNet framework treats robots as intelligent data interfaces, shifting learning from static offline datasets toward dynamic Physical AI powered by automated, online intervention.

02 / System Architecture

One loop from physical data to deployed robot behavior.

01

Embodied Data Ecology

Capture high-fidelity interaction streams across robots, humans, objects, and scenes.

02

Physical World Models

Learn actionable models that connect perception, contact, force, intent, and action.

03

Real-Robot Deployment

Deploy, intervene, evaluate, and feed real-world outcomes back into model improvement.

03 / Selected Research Physical Reasoning · Robot Learning · Benchmarks
Robot Learning

AetheRock

An arm-worn robot teaching system for force-guided vision-tactile learning.

Project
AetheRock robot teaching system
04 / Bridge Layer
InputVirtual simulation
InputReal-world interaction
OutputGeneral-purpose robotics

From world models to physical deployment.

RHOS builds a critical bridge between human-centric generative AI and the rigorous demands of physical environments. By fusing simulated experience with high-fidelity spatiotemporal data from real-world interactions, we work toward zero-shot generalization for unseen robots and unseen tasks.

05 / About RHOS

A physical AI company building robot foundation systems.

RHOS brings together world modeling, embodied data, motion control, and robotic hardware to develop systems that learn from real physical interaction.

We focus on the full feedback loop: collect, model, deploy, evaluate, and improve.

06 / Careers

Build physical intelligence with us.

World models · Data systems · Motion control · Hardware

Open roles