From a Binary Feature Matrix to Correlation Analysis: A Dual-Paradigm Classification of Global Robotics Research Objectives

Abstract

Robotics research encompasses a wide range of technical challenges and interdisciplinary approaches. This study introduces a dual-paradigm classification framework for organizing the stated objectives of 130 leading robotics laboratories across North America, Europe, Asia, and Australia. The framework includes a Problem-Based paradigm that addresses core technical challenges and a Solution-Based paradigm that focuses on methodological strategies. Each laboratory’s objectives are encoded into a 130 × 17 binary matrix, enabling large-scale quantitative analysis. Spearman correlation analysis, corrected using the Benjamini–Hochberg False Discovery Rate (FDR) method, reveals statistically significant co-occurrence trends. Key findings include the synergy between artificial intelligence and cognition, a hardware–software divide, and underexplored intersections between AI and physical system innovation. In addition to correlation heatmaps, visualizations such as PCA biplots and radial similarity networks reveal research gaps and collaboration opportunities. These insights can guide agencies, industry partners, and academic institutions in shaping future robotics research. By identifying promising scientific directions, stakeholders can strategically combine these areas to develop more advanced, integrated, and capable robotic systems. All data and analysis tools have been deposited on IEEE DataPort and GitHub to ensure transparency and reproducibility.