End to end learning of a multi-layered snn based on r-stdp for a target tracking snake-like robot

Zhenshan Bing, Zhuangyi Jiang, Long Cheng, Caixia Cai, Kai Huang, Alois Knoll

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 Scopus citations

Abstract

This paper introduces an end-to-end learning approach based on Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) for a multi-layered spiking neural network (SNN). As a case study, a snake-like robot is used as an agent to perform target tracking tasks on the basis of our proposed approach. Since the key of R-STDP is to use rewards to modulate synapse strengthens, we first propose a general way to propagate the reward back through a multi-layered SNN. Upon the proposed approach, we build up an SNN controller that drives a snake-like robot for performing target tracking tasks. We demonstrate the practicability and advantage of our approach in terms of lateral tracking accuracy by comparing it to other state-of-the-art learning algorithms for SNNs based on R-STDP.

Original languageEnglish
Title of host publication2019 International Conference on Robotics and Automation, ICRA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages9645-9651
Number of pages7
ISBN (Electronic)9781538660263
DOIs
StatePublished - May 2019
Event2019 International Conference on Robotics and Automation, ICRA 2019 - Montreal, Canada
Duration: 20 May 201924 May 2019

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2019-May
ISSN (Print)1050-4729

Conference

Conference2019 International Conference on Robotics and Automation, ICRA 2019
Country/TerritoryCanada
CityMontreal
Period20/05/1924/05/19

Fingerprint

Dive into the research topics of 'End to end learning of a multi-layered snn based on r-stdp for a target tracking snake-like robot'. Together they form a unique fingerprint.

Cite this