用脉冲神经网络让机器人更智能地避人，还省电近100倍。
SINRL: Socially Integrated Navigation with Reinforcement Learning using Spiking Neural Networks
- 混合SNN与ANN，用脉冲网络做决策，人工网络评估策略
- 在社交导航任务中能耗降低约1.69个数量级
- 适合研究神经形态计算与智能机器人交互的学者
将自主移动机器人融入人类环境需要类人的决策能力以及能效高、事件驱动的计算。尽管已有进展，由于训练不稳定，神经形态方法很少应用于深度强化学习（DRL）导航。我们提出一种混合社会融合式DRL Actor-Critic方法：在Actor中使用脉冲神经网络（SNN），Critic中使用人工神经网络（ANN），并结合神经形态特征提取器以捕捉人群动态和人机交互的时序特征。该方法显著提升了社交导航性能，并使估算能耗降低了约1.69个数量级。
原文摘要 · Abstract (English)
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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