Many-joint robot arm control with recurrent spiking neural networks

Manuel Traub, Robert Legenstein, Sebastian Otte

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

Abstract

In the paper, we show how scalable, low-cost trunk-like robotic arms can be constructed using only basic 3D-printing equipment and simple electronics. The design is based on uniform, stackable joint modules with three degrees of freedom each. Moreover, we present an approach for controlling these robots with recurrent spiking neural networks. At first, a spiking forward model learns motor-pose correlations from movement observations. After training, intentions can be projected back through unrolled spike trains of the forward model essentially routing the intention-driven motor gradients towards the respective joints, which unfolds goal-direction navigation. We demonstrate that spiking neural networks can thus effectively control trunk-like robotic arms with up to 75 articulated degrees of freedom with near millimeter accuracy.

Original languageEnglish
Title of host publicationIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
Pages4918-4925
Number of pages8
ISBN (Electronic)9781665417143
DOIs
Publication statusPublished - 2021
Event2021 IEEE/RSJ International Conference on Intelligent Robots and Systems : IROS 2021 - Virtuell, Czech Republic
Duration: 27 Sep 20211 Oct 2021

Conference

Conference2021 IEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleIROS 2021
Country/TerritoryCzech Republic
CityVirtuell
Period27/09/211/10/21

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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