Global research trends and thematic evolution in humanoid robotics: a scientometric and text mining study

Abstract Purpose This paper aims to map the global landscape of humanoid robotics research (2000–2024) using scientometric techniques to identify key trends, thematic evolutions, and influential contributors, thereby guiding future research and policy directions. Design/methodology/approach The study retrieved 7065 Scopus-indexed publications on humanoid robots, then applied bibliometric analyses and text mining, including topic modelling (LDA), co-word mapping, citation and authorship metrics (Lotka’s Law), and country-level contributions. Visualisation tools (i.e. Google Colab) were used to illustrate publication trends, thematic clusters, and collaboration networks. Findings The results reveal a steady annual increase in publications, with notable surges post-2010. Key research themes include human–robot interaction (HRI), locomotion, AI integration, and ethical/social implications. The US, Japan, and Germany emerged as leading contributors, while co-authorship analysis highlighted a skewed distribution among prolific scholars. Topic modelling showed growing emphasis on social robotics and ethical frameworks. Research limitations/implications The analysis is limited to publications indexed in English within Scopus, potentially excluding significant regional or non-English research. Citation lag may under-represent recent contributions. Future studies should extend to additional databases and include non-English sources to capture diverse scholarly perspectives. Practical implications Insights inform funding agencies on strategic investments in themes, guide policymakers in developing robotics ethics regulations, and help researchers identify emerging subfields and collaboration opportunities. Originality/value This is one of the first quantitative scientometric studies focused exclusively on humanoid robotics over 25 years, offering a comprehensive, data-driven panorama of scholarly activity. This work advances knowledge by linking research outputs with thematic dynamics and providing actionable recommendations.