Allostatic control for robot behaviour regulation: An extension to path planning

Abstract

Rodents are optimal real-world foragers that can smoothly regulate behaviors like homing and exploration combined with more elaborate abilities as food source localization. Here we investigate a robot based model that implements the self-regulatory processes that underly optimal foraging of rodents in unknown environments and is also able to combine it with goal directed behaviors. Behavior is decomposed into minimal homeostatic subsystems that regulate themselves through the local perception/detection of a gradient. On a higher level, the allostatic control orchestrates the interaction of the different homeostatic modules allowing it to dynamically manage the interactions between the desired values of each subsystem to achieve stability on a meta behavioral level. In this case, we show that overall behavioral stability can be achieved. We validate our model by comparing the behavior of both simulated and real robots with that of rodents. Our next step is then to justify gradients as a valid biological assumption by giving a biologically plausible process for generating them from a cognitive map, in this case, a set of approximated hippocampal place cells. We finally formulate path planning (used for goal reaching, e.g. food source localization) in the same context of a gradient map generation that can be then inserted as an additional subsystem of the higher meta level allostatic control.