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arXiv · 2512.07266

SINRL: Socially Integrated Navigation with Reinforcement Learning using Spiking Neural Networks

Florian Tretter·Daniel Flögel·Alexandru Vasilache·Max Grobbel·Jürgen Becker·Sören Hohmann
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Abstract

Integrating autonomous mobile robots into human environments requires human-like decision-making and energy-efficient, event-based computation. Despite progress, neuromorphic methods are rarely applied to Deep Reinforcement Learning (DRL) navigation approaches due to unstable training. We address this gap with a hybrid socially integrated DRL actor-critic approach that combines Spiking Neural Networks (SNNs) in the actor with Artificial Neural Networks (ANNs) in the critic and a neuromorphic feature extractor to capture temporal crowd dynamics and human-robot interactions. Our approach enhances social navigation performance and reduces estimated energy consumption by approximately 1.69 orders of magnitude.

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Florian Tretter, Daniel Flögel, Alexandru Vasilache, Max Grobbel, Jürgen Becker, Sören Hohmann. 2025-12-08. SINRL: Socially Integrated Navigation with Reinforcement Learning using Spiking Neural Networks. https://arxiv.org/abs/2512.07266

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