SINRL: Socially Integrated Navigation with Reinforcement Learning using Spiking Neural Networks
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917132460097536 |
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| author | Tretter, Florian Flögel, Daniel Vasilache, Alexandru Grobbel, Max Becker, Jürgen Hohmann, Sören |
| author_facet | Tretter, Florian Flögel, Daniel Vasilache, Alexandru Grobbel, Max Becker, Jürgen Hohmann, Sören |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_07266 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SINRL: Socially Integrated Navigation with Reinforcement Learning using Spiking Neural Networks Tretter, Florian Flögel, Daniel Vasilache, Alexandru Grobbel, Max Becker, Jürgen Hohmann, Sören Robotics Artificial Intelligence Systems and Control 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. |
| title | SINRL: Socially Integrated Navigation with Reinforcement Learning using Spiking Neural Networks |
| topic | Robotics Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2512.07266 |