Gridbot: An autonomous robot controlled by a Spiking Neural Network mimicking the brain's navigational system

TL;DR

This paper presents Gridbot, an autonomous robot controlled by a biologically inspired spiking neural network that mimics the brain's navigational system, capable of mapping unknown environments despite hardware imperfections.

Contribution

The work introduces a novel SNN model based on neuroscience findings that effectively controls a robot for spatial mapping without relying on all-to-all connectivity.

Findings

SNN enables robust autonomous navigation in unknown environments.

The model compensates for visual input loss and hardware imperfections.

Demonstrates feasibility of neuromorphic hardware implementation.

Abstract

It is true that the "best" neural network is not necessarily the one with the most "brain-like" behavior. Understanding biological intelligence, however, is a fundamental goal for several distinct disciplines. Translating our understanding of intelligence to machines is a fundamental problem in robotics. Propelled by new advancements in Neuroscience, we developed a spiking neural network (SNN) that draws from mounting experimental evidence that a number of individual neurons is associated with spatial navigation. By following the brain's structure, our model assumes no initial all-to-all connectivity, which could inhibit its translation to a neuromorphic hardware, and learns an uncharted territory by mapping its identified components into a limited number of neural representations, through spike-timing dependent plasticity (STDP). In our ongoing effort to employ a bioinspired SNN-controlled robot to real-world spatial mapping applications, we demonstrate here how an SNN may robustly control an autonomous robot in mapping and exploring an unknown environment, while compensating for its own intrinsic hardware imperfections, such as partial or total loss of visual input.