Ontology Neural Network and ORTSF: A Framework for Topological Reasoning and Delay-Robust Control

TL;DR

This paper presents a unified framework combining ontology neural networks and a semantic fabric to enhance topological reasoning and delay-robust control in autonomous robots, addressing semantic and temporal challenges.

Contribution

It introduces a novel architecture integrating relational semantic reasoning with delay-aware control, bridging the gap between cognitive semantics and robust control in robotics.

Findings

Preserves relational semantics during scene evolution.

Ensures control signal continuity despite delays.

Unifies semantic reasoning with delay-robust control.

Abstract

The advancement of autonomous robotic systems has led to impressive capabilities in perception, localization, mapping, and control. Yet, a fundamental gap remains: existing frameworks excel at geometric reasoning and dynamic stability but fall short in representing and preserving relational semantics, contextual reasoning, and cognitive transparency essential for collaboration in dynamic, human-centric environments. This paper introduces a unified architecture comprising the Ontology Neural Network (ONN) and the Ontological Real-Time Semantic Fabric (ORTSF) to address this gap. The ONN formalizes relational semantic reasoning as a dynamic topological process. By embedding Forman-Ricci curvature, persistent homology, and semantic tensor structures within a unified loss formulation, ONN ensures that relational integrity and topological coherence are preserved as scenes evolve over time. The ORTSF transforms reasoning traces into actionable control commands while compensating for system delays. It integrates predictive and delay-aware operators that ensure phase margin preservation and continuity of control signals, even under significant latency conditions. Empirical studies demonstrate the ONN + ORTSF framework's ability to unify semantic cognition and robust control, providing a mathematically principled and practically viable solution for cognitive robotics.