Cognitive Synergy Architecture: SEGO for Human-Centric Collaborative Robots

TL;DR

This paper introduces SEGO, a cognitive mapping architecture that integrates perception, semantic reasoning, and explanation generation to enhance human-centric collaborative robots.

Contribution

SEGO provides a unified framework combining geometric perception, semantic reasoning, and explanation generation for improved robot understanding.

Findings

Real-time, semantically coherent mapping achieved.

Dynamic cognitive scene graphs represent spatial and semantic relations.

Integration of SLAM, deep learning, and ontology reasoning.

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.