In behavioral motor coordination and interaction it is a fundamental challenge how an agent can learn to perceive and act in unknown and dynamic environments. At present, it is not clear how an agent can - without any explicitly predefined knowledge - acquire internal representations of the world while interacting with the environment. To meet this challenge, we propose a biologically based cognitive architecture called Distributed Adaptive Control (DAC). DAC is organized in three different, tightly coupled, layers of control: reactive, adaptive and contextual. DAC based systems are self-contained and fully grounded, meaning that they autonomously generate representations of their primary sensory inputs, hence bootstrapping their behavior form simple to advance interactions. Following this approach, we have previously identified a novel environmentally mediated feedback loop in the organization of perception and behavior, i.e. behavioral feedback. Additionally, we could demonstrated that the dynamics of the memory structure of DAC, acquired during a foraging task, are equivalent to a Bayesian description of foraging. In this chapter we present DAC in a concise form and show how it is allowing us to extend the different subsystems to more biophysical detailed models. These further developments of the DAC architecture, not only allow to better understand the biological systems, but moreover advance DACs behavioral capabilities and generality.