As more is becoming understood about how the brain represents and computes with high-level spatial information, the prospect of constructing biologically-inspired robot controllers using these spatial representations has become apparent. Grid cells are particularly interesting in this regard, as they provide a general coordinate system of space. Artificial neural network models of grid cells show the ability to perform path integration, but important for a robot is also the ability to calculate

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... the direction from the current location, as indicated by the path integrator, to a remembered goal. Present models for goal-directed navigation using grid cells have used a simulating approach, where the networks are required to actively test successive locations along linear trajectories emanating from the current location. This paper presents a passive model, where differences between multiscale grid cell representations of the present location and the goal are used to calculate a goal-direction signal directly. The model successfully guides a simulated agent to its goal, showing promise for implementing the system on a real robot in the future. Some possible implications for neuroscientific studies on the goal-direction signal in the entorhinal/subicular region are briefly discussed.