Concept-Guided Exploration: Building Persistent, Actionable Scene Graphs

The perception of 3D space by mobile robots is rapidly moving from flat metric 1 grid representations to hybrid metric-semantic graphs built from human-interpretable 2 concepts. While most approaches first build metric maps and then add semantic layers, 3 we explore an alternative concept-first architecture where spatial understanding emerges 4 from asynchronous concept agents that directly instantiate and manage semantic entities. 5 Our robot employs two spatial concepts—room and door—implemented as autonomous 6 processes within a cognitive distributed architecture. These concept agents cooperatively 7 build a shared scene graph representation of indoor layouts through active exploration 8 and incremental validation. The key architectural principle is hierarchical constraint 9 propagation: room instantiation provides geometric and semantic priors that constrain and 10 improve door detection within wall boundaries. The resulting structure is maintained by a 11 complementary functional principle: prediction-matching loops. This approach builds an 12 actionable, human-interpretable spatial representation without relying on a pre-existing 13 global metric map, enabling scalable operation and persistent, task-relevant understanding 14 in structured indoor environments.