Integrating neuroscience-based models towards an autonomous biomimetic Synthetic Forager

Foraging can be described as goal-oriented exploration for resources. It exemplifies how animals coordinate complex sensory and effector systems under varying environmental conditions. To emulate the foraging capabilities of natural systems is a major goal for robotics. Therefore, foraging is an excellent paradigm to benchmark novel autonomous control strategies. Here we describe the biomimetic control architecture of the Synthetic Forager (SF), an effort to integrate multiple biologically constrained models of specific perceptual and cognitive processes pertaining to foraging into one general autonomous robot controller. This proposal is built upon the well-established Distributed Adaptive Control (DAC) framework and brings together neuroscience-based models of decision-making, multi-modal sensory processing, localization and mapping and allostatic behavioral control. To show the potential of the SF model we used it to control a high-mobility wheeled robotic platform in three behavioral tasks similar to experimental protocols applied to rodents. We show that the robot can reliably perform cue detection, rule learning and goal-oriented navigation in open environments. We propose that this approach to robotics allows both the study of embodied neuroscience models and the transfer of brain based principles to robotic systems.