Embodied Data Ecology
Capture high-fidelity interaction streams across robots, humans, objects, and scenes.
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.
Unit
RHOS
Title
Physical Large Model
Dwg No.
RHO-001 · REV B
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 / RHOS LOOP
ROBOT · HUMAN · OBJECT · SCENE
Physical Causal Model
Learn actionable world models that connect perception, contact, force, and intent.
Robot Flywheel
Deploy, intervene, evaluate, and feed real-world outcomes back into model improvement.
03 / 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.