2026-08-24 · Applied Sciences · 6 citations

Concept-Guided Exploration: Building Persistent, Actionable Scene Graphs

Noé Zapata, Gerardo Pérez, Alejandro Torrejón, Pedro Núñez, Pablo Bustos

Published at Applied Sciences (the arXiv record still lists it as a preprint). 6 citations, 1 of them influential, as of the last refresh.

Abstract

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

Ten-minute slide kit

Six slides is the whole talk: what was broken, what people tried, what these authors did, what the numbers say, where it falls over, and the sentence people should remember.

SLIDE 1

The problem

Robots operating in human environments benefit from semantically rich, shared representations of their surroundings. In recent years, 3D scene graphs (3DSGs) have been used for this purpose, as complex data structures that represent scene objects as nodes and their various relationships as edges Armeni et al. \(2019\). When used to describe large fragments of space, they are typically organised hierarchically, with higher levels encompassing larger spatial aggregations Hughes et al.

SLIDE 2

What came before

Not recoverable from the parsed text. Read this section in the paper yourself.

SLIDE 3

The method

CORTEX is a cognitive robotics architecture initially designed to explore how the robot, the environment, and their interaction can be efficiently represented and anchored. A general scheme is shown in It is organised into two blocks: the cognitive level, a collection of specialised memories interconnected by processes called *agents*, which are responsible for exchanging information among them, and the subcognitive level, which maintains a bidirectional connection with the robot's body. Additional details on the…

SLIDE 4

What they measured

We have designed a series of preliminary experiments to evaluate the architecture using the Webots simulator. The scenarios include a digital replica of our **Shadow** mobile robot Torrejón et al. \(2024\), with realistic white noise added to the synthetic LiDAR and simulated delays in command execution. All detection parameters and thresholds used in the following experiments, such as those in Algorithm 1 and 2, were tuned empirically to suit this specific robotic setup and the characteristics of our test…

SLIDE 5

Where it breaks

The architecture explicitly acknowledges operational boundaries. Severely cluttered environments where furniture occludes wall geometry, non-rectangular spaces that violate Manhattan assumptions, or dynamic scenes with moving obstacles may make successful concept instantiation difficult. Rather than attempting exhaustive coverage of edge cases, the system maintains probabilistic beliefs and can defer instantiation when evidence remains

SLIDE 6

One-line takeaway

A novel algorithm that detects potentially hazardous situations for humans and selects appropriate robotic actions to eliminate these dangers in real time is proposed, offering a promising approach to enhancing human–robot interaction in potentially hazardous environments.

Assembled from the paper's own PDF, parsed with its layout intact so tables and equations survive, 98,613 characters of it, then split on the paper's own section headings. Extractive, not generated: every sentence here is lifted from the paper. Check it before you present it.

Figures worth putting on a slide

Figure 1: An overview of the CORTEX architecture with the elements used in this work. The sub-cognitive level on the left encompasses low-level perceptive and control components. T
Figure 2: Reference frames used for the elements in the scene graph. On the left, the grey rectangle represents the model room that best fits the corners. *T w <sup>R</sup>* denote
Figure 3: Graph state transition. The robot starts as the graph's initial frame (*root is a dummy node*) and transforms to hang from the new room when it is inserted in the graph.
Figure 4: Flow chart showing the life-cycle of a concept. The two outgoing lines of the first decision box cover the insert new instance and update existing instances situations.
Figure 5: F*room* components diagram. Before the room is established, two reference systems are established: the origin reference system and the robot reference system. In the case
Figure 6: Main stages in constructing the scene graph during exploration, reflecting transitions as different concept instances are initialised and affordances are executed. See te