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 language | English |
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Title of host publication | IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021 |
Pages | 4918-4925 |
Number of pages | 8 |
ISBN (Electronic) | 9781665417143 |
DOIs | |
Publication status | Published - 2021 |
Event | 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems : IROS 2021 - Virtuell, Czech Republic Duration: 27 Sept 2021 → 1 Oct 2021 |
Conference
Conference | 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems |
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Abbreviated title | IROS 2021 |
Country/Territory | Czech Republic |
City | Virtuell |
Period | 27/09/21 → 1/10/21 |
ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Computer Vision and Pattern Recognition
- Computer Science Applications