Detecting Affordances by Visuomotor Simulation
This work addresses the challenge of enabling robots to interpret object affordances for navigation, though it is incremental as it builds on existing simulation-based approaches.
The authors tackled the problem of detecting object affordances (behavioral meanings) in visual perception by proposing a cognitive architecture based on internal visuomotor simulation, and applied it to a mobile robot to reliably distinguish between passable corridors and non-passable dead ends in real-world obstacle arrangements.
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.