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Learning to walk -- i.e., learning locomotion under performance and energyconstraints continues to be a challenge in legged robotics. Methods such asstochastic gradient, deep reinforcement learning (RL) have been explored forbipeds, quadrupeds and hexapods. These techniques are computationally intensiveand often prohibitive for edge applications. These methods rely on complexsensors and pre-processing of data, which further increases energy and latency.Recent advances in spiking neural networks (SNNs) promise a significantreduction in computing owing to the sparse firing of neuros and has been shownto integrate reinforcement learning mechanisms with biologically observed spiketime dependent plasticity (STDP). However, training a legged robot to walk bylearning the synchronization patterns of central pattern generators (CPG) in anSNN framework has not been shown. This can marry the efficiency of SNNs withsynchronized locomotion of CPG based systems providing breakthrough end-to-endlearning in mobile robotics. In this paper, we propose a reinforcement basedstochastic weight update technique for training a spiking CPG. The whole systemis implemented on a lightweight raspberry pi platform with integrated sensors,thus opening up exciting new possibilities.

Learning to Walk: Spike Based Reinforcement Learning for Hexapod Robot Central Pattern Generation