Year: 2025
Type: conference-paper
Abstract: While there are adequate tools available to simulate Spiking Neural Networks (e.g. Brian2, snnTorch), as well as the tools for simulating robots and their environments, there remains a need for integrated tools that enable researchers to jointly simulate realistic brain models, robots, and sensory-rich environments. This work introduces a comprehensive neuromorphic robotic system, which combines neuromorphic computing with neuromorphic (and conventional) sensory and motor devices. We emulate the neuromorphic computing on a conventional low-power CPU, specifically a Virtual Machine on a Raspberry Pi 5, integrating Python and specialised packages for real-time Spiking Neural Networks (SNN) simulations. We achieve: (i) a cost-effective alternative to dedicated neuromorphic hardware, (ii) built-in GPIO and USB ports for seamless sensor and motor interfacing. We have built a demonstrator system: a robotic goalkeeper, using a DVS camera, a digital servo motor, and a touch sensor for a reward signal. The SNN uses a combination of unsupervised and supervised (reinforcement) learning. The system off-line and on-line learning was demonstrated, and some performance metrics reported.
Institution: University of West London
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4411724878