  The term "affordance" denotes the behavioral meaning of objects. We propose a
cognitive architecture for the detection of affordances in the visual modality.
This model is based on the internal simulation of movement sequences. For each
movement step, the resulting sensory state is predicted by a forward model,
which in turn triggers the generation of a new (simulated) motor command by an
inverse model. Thus, a series of mental images in the sensory and in the motor
domain is evoked. Starting from a real sensory state, a large number of such
sequences is simulated in parallel. Final affordance detection is based on the
generated motor commands. We apply this model to a real-world mobile robot
which is faced with obstacle arrangements some of which are passable (corridor)
and some of which are not (dead ends). The robot's task is to detect the right
affordance ("pass-through-able" or "non-pass-through-able"). The required
internal models are acquired in a hierarchical training process. Afterwards,
the robotic agent is able to distinguish reliably between corridors and dead
ends. This real-world result enhances the validity of the proposed mental
simulation approach. In addition, we compare several key factors in the
simulation process regarding performance and efficiency.
