Self-learning neuromorphic robot based on reward-driven Spiking Neural Network

Russo, Nicola, Madsen, Thomas ORCID logoORCID: https://orcid.org/0000-0001-9354-0935 and Nikolic, Konstantin ORCID logoORCID: 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
Identifier: 10.1109/ISCAS56072.2025.11044049
Identifier: 10.1109/ISCAS56072.2025.11044049
Keywords: Robotics ; spiking neural networks ; neuromorphic computing ; neuromorphic hardware ; low-power systems
Subjects: Computing > Intelligent systems
Related URLs:
Depositing User: Mary Blomley
Date Deposited: 28 Jul 2025 11:59
Last Modified: 31 Jul 2025 08:03
URI: https://repository.uwl.ac.uk/id/eprint/13919

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