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

• URL: http://arxiv.org/abs/2512.07266v1
• Date: Mon, 08 Dec 2025 08:06:40 GMT
• Title: SINRL: Socially Integrated Navigation with Reinforcement Learning using Spiking Neural Networks
• Authors: Florian Tretter, Daniel Flögel, Alexandru Vasilache, Max Grobbel, Jürgen Becker, Sören Hohmann,
• Abstract summary: We present 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.<n>Our approach enhances social navigation performance and reduces estimated energy consumption by approximately 1.69 orders of magnitude.
• Score: 36.26984530753368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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