Cognitive Synergy Architecture: SEGO for Human-Centric Collaborative Robots

Abstract

This paper presents SEGO (Semantic Graph Ontology), a cognitive mapping architecture designed to integrate geometric perception, semantic reasoning, and explanation generation into a unified framework for human-centric collaborative robotics. SEGO constructs dynamic cognitive scene graphs that represent not only the spatial configuration of the environment but also the semantic relations and ontological consistency among detected objects. The architecture seamlessly combines SLAM-based localization, deep learning-based object detection and tracking, and ontology-driven reasoning to enable real-time, semantically coherent mapping.

A systematic experimental evaluation was conducted using the TUM RGB-D dataset, with frame rates ranging from 10 to 60 FPS. Results demonstrated that SEGO achieves significant improvements in semantic mapping quality up to 30 FPS, with the Semantic Recognition Quality Index (SRQI) increasing from 0.662 at 10 FPS to 0.703 at 30 FPS, beyond which gains plateau. This frame-rate-dependent behavior aligns with known limits of human perceptual integration, supporting SEGO’s suitability for intuitive human-robot interaction. Moreover, SEGO’s reasoning traceability enables transparent and interpretable decision- making, fostering trust and predictability in collaborative settings.

The study introduces novel metrics, including SRQI, violation rate, and relation entropy, to quantitatively assess semantic mapping performance. The results validate SEGO’s frame-rate- aware design and its capacity to deliver cognitively transparent mapping with computational efficiency. The architecture provides a principled foundation for future cognitive robotic systems requiring real-time semantic understanding, logical consistency, and explainable reasoning in complex, dynamic environments.