The field of robotics is moving towards more complex and human-like interaction strategies, with a focus on embodied navigation and social robot interaction. Researchers are exploring new approaches to enable robots to navigate and interact with their environment in a more intelligent and adaptive way. This includes the development of new algorithms and frameworks for multi-robot navigation, egocentric navigation, and social interaction. A key challenge in this area is the ability of robots to navigate constrained and cluttered environments, often while competing for space with other robots and humans. To address this challenge, researchers are proposing new taxonomies, definitions, and evaluation protocols to guide effective research moving forward. Notable papers in this area include:

• A survey that catalogs social mini-game solvers using a well-defined and unified taxonomy, classifying existing methods accordingly.
• A framework that predicts collision-free future trajectories from egocentric observations, with applications in humanoid robotics and assistive navigation.
• A comprehensive survey that introduces a formulation for embodied navigation, synthesizing the current state of the art and identifying critical open research challenges.