Russo, Nicola, Madsen, Thomas ORCID: https://orcid.org/0000-0001-9354-0935 and Nikolic, Konstantin ORCID: https://orcid.org/0000-0002-6551-2977 (2025) Self-learning neuromorphic robot based on reward-driven Spiking Neural Network. In: IEEE International Symposium on Circuits and Systems (ISCAS), 25-28 May 2025, London, UK.

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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.

Item Type:	Conference or Workshop Item (Paper)
ISSN:	2158-1525
ISBN:	9798350356830
Keywords:	robotics, spiking neural networks, neuromorphic computing, neuromorphic hardware, low-power systems
Subjects:	Computing > Software engineering
Related URLs:	https://2025.ieee-iscas.org/
Depositing User:	Nicola Russo
Date Deposited:	22 Jul 2025 13:42
Last Modified:	22 Jul 2025 13:47
URI:	https://repository.uwl.ac.uk/id/eprint/13904
Sustainable Development Goals:	Goal 10: Reduced Inequalities

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