On the Importance of Neural Membrane Potential Leakage for LIDAR-based Robot Obstacle Avoidance using Spiking Neural Networks

TL;DR

This paper demonstrates that tuning membrane leakage in spiking neural networks enables robot obstacle avoidance with LIDAR data to match the precision of traditional CNNs, highlighting the importance of neuron leakage in neuromorphic robotics.

Contribution

It provides the first focused study on how membrane potential leakage affects SNN accuracy in LIDAR-based obstacle avoidance for robots.

Findings

Tuning leakage constant improves SNN precision.

SNN with tuned leakage matches CNN control accuracy.

Open-source LIDAR dataset is released for future research.

Abstract

Using neuromorphic computing for robotics applications has gained much attention in recent year due to the remarkable ability of Spiking Neural Networks (SNNs) for high-precision yet low memory and compute complexity inference when implemented in neuromorphic hardware. This ability makes SNNs well-suited for autonomous robot applications (such as in drones and rovers) where battery resources and payload are typically limited. Within this context, this paper studies the use of SNNs for performing direct robot navigation and obstacle avoidance from LIDAR data. A custom robot platform equipped with a LIDAR is set up for collecting a labeled dataset of LIDAR sensing data together with the human-operated robot control commands used for obstacle avoidance. Crucially, this paper provides what is, to the best of our knowledge, a first focused study about the importance of neuron membrane leakage on the SNN precision when processing LIDAR data for obstacle avoidance. It is shown that by carefully tuning the membrane potential leakage constant of the spiking Leaky Integrate-and-Fire (LIF) neurons used within our SNN, it is possible to achieve on-par robot control precision compared to the use of a non-spiking Convolutional Neural Network (CNN). Finally, the LIDAR dataset collected during this work is released as open-source with the hope of benefiting future research.