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

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

23 Zitate (Scopus)

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.

OriginalspracheEnglisch
Titel2019 International Conference on Robotics and Automation, ICRA 2019
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten9645-9651
Seitenumfang7
ISBN (elektronisch)9781538660263
DOIs
PublikationsstatusVeröffentlicht - Mai 2019
Veranstaltung2019 International Conference on Robotics and Automation, ICRA 2019 - Montreal, Kanada
Dauer: 20 Mai 201924 Mai 2019

Publikationsreihe

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

Konferenz

Konferenz2019 International Conference on Robotics and Automation, ICRA 2019
Land/GebietKanada
OrtMontreal
Zeitraum20/05/1924/05/19

Fingerprint

Untersuchen Sie die Forschungsthemen von „End to end learning of a multi-layered snn based on r-stdp for a target tracking snake-like robot“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren