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.
@article{arxiv.2512.07266,
title = {SINRL: Socially Integrated Navigation with Reinforcement Learning using Spiking Neural Networks},
author = {Florian Tretter and Daniel Flögel and Alexandru Vasilache and Max Grobbel and Jürgen Becker and Sören Hohmann},
journal= {arXiv preprint arXiv:2512.07266},
year = {2025}
}