Bioinspired Dynamic Control of Amphibious Articulated Creatures with Spiking Neural Networks

Abstract: We present a biologically plausible, compact spiking neural network for controlling the crawling and swimming behaviors of amphibious creatures with articulated skeletons. Prior methods for learning efficient control policies for such creatures are resource-greedy, both in terms of computational time and energy requirements due to the high number of degrees of freedom introduced by the many joints present in the creature skeleton. Our approach takes a radical departure from prior work and exploits the physiology of amphibious creatures. Specifically, we emulate experimentally identified biological controllers for amphibious creatures with a network of spiking neurons, which alleviates the need for training altogether and can potentially provide the additional benefit of utilizing minimal resources in terms of energy. Our approach is robust and allows the amphibious creature to avoid both static and dynamic obstacles when exhibiting different movement patterns, and also adaptively control its swimming speed. Moreover, we show that the creature can seamlessly transition between crawling and swimming behaviors as it moves from land to water or vice-versa, similar to its real-world counterpart. Our approach presents an efficient and scalable alternative for producing vivid and lively motion, as we demonstrate through a complex scene where multiple amphibious creatures interact with each other, successfully avoiding collisions while moving across a pool of water. Our approach is generalizable to other creatures also, as we show through the design of a controller for a quadruped.