arXiv:2512.07266v1 Announce Type: cross
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.
Accelerometer-Derived Rest-Activity Rhythm Amplitude, Genetic Predisposition, and the Risk of Ischemic Heart Disease: Observational and Mendelian Randomization Study
Background: The rest-activity rhythm amplitude (RARA), as a fundamental human behavior, has been linked to various health conditions. However, its causal relationship with ischemic heart