HARMONIC: A Content-Centric Cognitive Robotic Architecture

2025-09-16

AI Summary

To address critical challenges in human-robot collaboration—including shallow semantic understanding, opaque decision-making, intent-deficient language interaction, data scarcity, and insufficient safety guarantees—this paper proposes a content-centric cognitive robot architecture. The architecture integrates semantic perception, cognitive modeling, intent recognition, and controllable natural language generation to enable interpretable semantic reasoning and human-like decision-making under formal safety constraints. A novel intent-driven language communication mechanism is introduced to enhance system transparency and foster human trust. The architecture is rigorously validated on both high-fidelity simulation and physical robot platforms. Two proof-of-concept implementations demonstrate significant improvements in collaborative safety, task execution quality, and overall human-robot coordination efficiency.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Natural Language Processing: Safety and Robustness
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

This paper introduces HARMONIC, a cognitive-robotic architecture designed for robots in human-robotic teams. HARMONIC supports semantic perception interpretation, human-like decision-making, and intentional language communication. It addresses the issues of safety and quality of results; aims to solve problems of data scarcity, explainability, and safety; and promotes transparency and trust. Two proof-of-concept HARMONIC-based robotic systems are demonstrated, each implemented in both a high-fidelity simulation environment and on physical robotic platforms.

Problem

• Addresses safety and quality in human-robot teams
• Solves data scarcity, explainability, and safety issues
• Promotes transparency and trust through cognitive architecture

Innovation

• Content-centric cognitive robotic architecture
• Semantic perception and human-like decision-making
• Intentional language communication for transparency