Neuromorphic Adaptive Body Leveling in a Bioinspired Hexapod Walking Robot

Abstract

In the past few decades, bioinspired hexapod walking robots have attracted increasing attention, mainly due to their potential to efficiently traverse rough terrains. Recently, neuromorphic (brain-inspired) robotic control has been shown to outperform conventional control paradigms in stochastic environments. In this work, we propose a neuromorphic adaptive body leveling algorithm for a hexapod walking robot during transversal over multi-leveled terrain. We demonstrate adaptive control with distributed accelerator-driven neuro-integrators with only a few thousand spiking neurons. We further propose a framework for the integration of MuJoCo, a modeling environment, and Nengo, a spiking neural networks compiler, for efficient evaluation of neuromorphic control over high degrees of freedom robotic systems in realistic physics-driven scenarios.