  This paper introduces a new perspective of intelligent robots and systems
control. The presented and proposed cognitive model: Memory, Learning and
Recognition (MLR), is an effort to bridge the gap between Robotics, AI,
Cognitive Science, and Neuroscience. The currently existing gap prevents us
from integrating the current advancement and achievements of these four
research fields which are actively trying to define intelligence in either
application-based way or in generic way. This cognitive model defines
intelligence more specifically, parametrically and detailed. The proposed MLR
model helps us create a general control model for robots and systems
independent of their application domains and platforms since it is mainly based
on the dataset provided for robots and systems controls. This paper is mainly
proposing and introducing this concept and trying to prove this concept in a
small scale, firstly through experimentation. The proposed concept is also
applicable to other different platforms in real-time as well as in simulation.
