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A Framework for Active Vision-Based Robot Planning using Spiking Neural Networks

Robust and energy-efficient robot planning is of utmost importance for mobile robots since the dynamic changes of the environment entail robotic agents with high adaptation capacities, so as to excel in their tasks. In this work, we introduce a hybrid spiking and deep neural network architecture for actor-critic control of a 6-DOF robot arm. Our method firstly involves autonomous object detection via active vision exploration and thereafter, the entire hybrid architecture is described. In specific, the actor utilises an integrated-and-fire model for action generation, while the critic a deep neural one for action evaluation. Lastly, the benefits of this approach in terms of energy efficiency are extensively discussed.