  The ability to automatically learn movements and behaviors of increasing
complexity is a long-term goal in autonomous systems. Indeed, this is a very
complex problem that involves understanding how knowledge is acquired and
reused by humans as well as proposing mechanisms that allow artificial agents
to reuse previous knowledge. Inspired by Jean Piaget's theory's first three
sensorimotor substages, this work presents a cognitive agent based on CONAIM
(Conscious Attention-Based Integrated Model) that can learn procedures
incrementally. Throughout the paper, we show the cognitive functions required
in each substage and how adding new functions helps address tasks previously
unsolved by the agent. Experiments were conducted with a humanoid robot in a
simulated environment modeled with the Cognitive Systems Toolkit (CST)
performing an object tracking task. The system is modeled using a single
procedural learning mechanism based on Reinforcement Learning. The increasing
agent's cognitive complexity is managed by adding new terms to the reward
function for each learning phase. Results show that this approach is capable of
solving complex tasks incrementally.
