The field of social navigation and human-robot interaction is rapidly evolving, with a focus on developing innovative methods for safe and efficient navigation in complex social environments. Recent research has explored the use of topological features, neural networks, and reinforcement learning to improve robot navigation and interaction with humans. Notably, the development of novel datasets and frameworks for learning social navigation forces and modeling human behavior has advanced the field. Furthermore, the integration of uncertainty quantification and preference learning has enabled more effective human-robot collaboration. Notable papers include: Safe and Efficient Social Navigation through Explainable Safety Regions Based on Topological Features, which proposes a novel approach for safe and efficient social navigation using topological features. Perspective-Shifted Neuro-Symbolic World Models: A Framework for Socially-Aware Robot Navigation presents a neuro-symbolic model-based reinforcement learning architecture for social navigation, addressing the challenge of belief tracking in partially observable environments. TAGA: A Tangent-Based Reactive Approach for Socially Compliant Robot Navigation Around Human Groups introduces a modular reactive mechanism that enhances group-awareness capabilities in robot navigation.