Modernising Reinforcement Learning-Based Navigation for Embodied Semantic Scene Graph Generation 事件

Modernising Reinforcement Learning-Based Navigation for Embodied Semantic Scene Graph Generation arXiv:2603.25415v2 Announce Type: replace Abstract: Semantic world models enable embodied agents to reason about objects, relations, and spatial context beyond purely geometric representations. In Organic Computing, such models are a key enabler for objective-driven self-adaptation under uncertainty and resource constraints. The core challenge is to acquire observations maximising model quality and