Neural State Machines for Robust Learning and Control of Neuromorphic Agents

Abstract

Mixed-signal analog/digital neuromorphic circuits are characterized by ultra-low power consumption, real-time processing abilities, and low-latency response times. These features make them promising for robotic applications that require fast and power-efficient computing. However, due to the device mismatch and variability present in these circuits, developing architectures that can perform complex computations in a robust and reproducible manner is quite challenging. In this paper, we present a spiking neural network architecture implemented using these neuromorphic circuits, that enables reliable control of an autonomous agent as well as robust learning and recognition of visual patterns in a noisy real-world environment. While learning is implemented with a software algorithm running with a chip-in-the-loop setup, the inference and motor control processes are implemented exclusively by the neuromorphic processor, situated on the neuromorphic agent. In addition to this processor device, the agent comprises a dynamic vision sensor which produces spikes as it interacts with the environment in real-time. We show how the robust learning and reliable control properties of the system arise out of a recently proposed neural computational primitive denoted as Neural State Machine (NSM). We describe the features of the NSMs used in this context and demonstrate the agent's real-time robust perception and action behavior with experimental results.