# Many-Joint Robot Arm Control with Recurrent Spiking Neural Networks

arXiv: https://arxiv.org/abs/2104.04064 · HTML: https://mlsanity.com/paper/2104.04064/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2021.

## Built on by (most cited first, 3 of 3)

- [Closed-form control with spike coding networks](https://mlsanity.com/paper/2212.12887/) (2022)
- [Binding Dancers Into Attractors](https://mlsanity.com/paper/2206.02558/) (2022)
- [Spiking Neural Networks for Continuous Control via End-to-End Model-Based Learning](https://mlsanity.com/paper/2509.05356/) (2025)

## Abstract (arXiv)

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.