{"ID":2831551,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.07266","arxiv_id":"2512.07266","title":"SINRL: Socially Integrated Navigation with Reinforcement Learning using Spiking Neural Networks","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.","short_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...","url_abs":"https://arxiv.org/abs/2512.07266","url_pdf":"https://arxiv.org/pdf/2512.07266v1","authors":"[\"Florian Tretter\",\"Daniel Flögel\",\"Alexandru Vasilache\",\"Max Grobbel\",\"Jürgen Becker\",\"Sören Hohmann\"]","published":"2025-12-08T08:06:40Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"eess.SY\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}