Unsupervised learning of reflexive and action-based affordances to model navigational behavior

Here we model animat navigation in a real world environment by using place cell as a sensory representation. The cells’ place fields divided the environment into discrete states. The robot learns knowledge of the environment by memorizing the sensory outcome of its motor actions. This was composed of a central process, learning the probability of state-to-state transitions by motor actions and a distal processing routine, learning the extent to which these state-to-state transitions are caused by sensory-driven reflex behavior (obstacle avoidance). Navigational decision making integrates central and distal learned environmental knowledge to select an action that leads to a goal state. Differentiating distal and central processing increases the behavioral accuracy of the selected actions. We claim that the system can easily be expanded to model other behaviors, using alternative definitions of states and actions.