What i am trying to reach :
AATM started as a fundamental research project: can artificial cognition emerge from architecture, rather than from explicitly programmed cognitive functions?
The project has now found a major application axis: embodied intelligence for robotics.
One of the hardest problems in this field is not perception.
It is not movement.
It is not even learning.
It is internal regulation.
In living organisms, homeostasis is not a simple configuration file. The stability of internal states is the result of billions of years of evolution. Trying to manually find the perfect internal balance for one artificial organism is likely to be fragile, slow and structurally risky.
AATM changes the scaling strategy.
The goal is not to build one perfect artificial organism.
The goal is to build populations of imperfect blobs.
Each blob is a small autonomous sensorimotor unit: it has its own internal state, its own perception loop, its own movement, its own prediction errors, its own reinforcement dynamics and its own mini mammalian-inspired brain.
Some blobs may become too exploratory.
Some may become too conservative.
Some may collapse.
Some may become apathetic.
Some may discover useful behavioral patterns.
That is not a failure of the architecture.
That is the tuning surface.
Instead of fine-tuning one monolithic agent from the inside, AATM introduces a higher layer above the population. This layer can observe blobs over time, compare their behaviors, modify their parameters, duplicate useful configurations, remove unstable ones and consolidate what emerges across the swarm.
The higher layer does not need to have the same homeostasis as the blobs.
The blobs are driven by local regulation: action, energy, tension, prediction, novelty, saturation.
The upper layer is driven by population-level regulation: diversity, stability, coverage, robustness and collective performance.
This is the key shift.
AATM does not try to solve embodied intelligence by putting everything into one body and hoping the internal state remains stable.
It turns the problem into a distributed architecture:
local embodied agents below,
structural consolidation above.
This makes failure local instead of global.
It allows fine-tuning without restarting the whole system.
It creates a path from digital demonstrators to robotic swarms.
And it gives embodied AI a scalable architecture beyond the monolithic model.
What started as fundamental research into artificial cognition is becoming a practical path toward robotizable artificial organisms: imperfect by design, tunable by population, and scalable through collective consolidation.