Spatial Separation

Spatial separation, in various contexts, focuses on optimizing the distinctness or distance between entities to improve performance or safety. Current research explores this through diverse approaches, including embedding space manipulation in machine learning to mitigate catastrophic forgetting, decentralized Voronoi-based algorithms for multi-robot coordination, and geometric distance-based uncertainty estimation. These advancements have implications across fields, from improving the reliability of lifelong learning systems to enhancing the safety and efficiency of autonomous systems like drones and multi-robot teams.