Ultra-low energy neuromorphic device based navigation approach for biomimetic robots

This paper discusses our work on developing synaptic memory devices based neuromorphic platform for biomimetic robot navigation. We recently developed a Spike Timing Dependent Plasticity (STDP) based learning scheme for robots using mathematical model of memristive devices [1]. We demonstrated the potential of that approach by applying it to navigate a two-wheeled differential drive robot in an environment cluttered with obstacles. In this work, experimentally derived device models for synaptic memory devices are used to extend that learning scheme. Our results indicate that the proposed approach can outperform conventional algorithms in terms of accuracy, realtime unsupervised control, and energy-efficiency.