mlsanityData to 14 Oct 2026

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

arXiv 2512.07266 ↗First posted 8 Dec 2025cs.RO cs.AI cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 12 of its 29 references that are in the map. First posted on arXiv 8 Dec 2025.

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12 of 29 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Socially Integrated Navigation: A Social Acting Robot with Deep Reinforcement Learning14 Mar

Other references · 11

  1. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  2. 2018Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learningcited by 175
  3. 2016Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learningcited by 118
  4. 2021Core Challenges of Social Robot Navigation: A Surveycited by 106
  5. 2023Principles and Guidelines for Evaluating Social Robot Navigation Algorithmscited by 56
  6. 2019Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learningcited by 49
  7. 2020Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Controlcited by 49
  8. 2020Reinforcement co-Learning of Deep and Spiking Neural Networks for Energy-Efficient Mapless Navigation with Neuromorphic Hardwarecited by 36
  9. 2023SICNav: Safe and Interactive Crowd Navigation using Model Predictive Control and Bilevel Optimizationcited by 14
  10. 2022Neuro-Planner: A 3D Visual Navigation Method for MAV with Depth Camera based on Neuromorphic Reinforcement Learningcited by 7
  11. 2019Dynamic Power Management for Neuromorphic Many-Core Systemscited by 5

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  1. 2025Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches
  2. 2025SpikeRL: A Scalable and Energy-efficient Framework for Deep Spiking Reinforcement Learning
  3. 2026Spiking Neural Networks for Continuous Control: Neuromorphic Reinforcement Learning in Conventional Computing
  4. 2026Performance-Preserving Online Adaptation in Social Navigation via Diffusion Steering
  5. 2026Towards Socially Compliant Navigation in Deep Reinforcement Learning via Proxemics-Based Reward Modeling
  6. 2024MeSA-DRL: Memory-Enhanced Deep Reinforcement Learning for Advanced Socially Aware Robot Navigation in Crowded Environments
  7. 2024Socially-Aware Robot Navigation Enhanced by Bidirectional Natural Language Conversations Using Large Language Models
  8. 2024Context-aware Mamba-based Reinforcement Learning for social robot navigation