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@article{205542,
        author = {Ranveer Singh and Aryan Somanna and Sidhesh Mishra and Shrey Jauhari and Abhijeet Pandey and Ridhuvarshini},
        title = {Predicting AI Maturity and Commercial Readiness of Humanoid Robotic Systems: A Machine Learning Analysis of the Global Humanoid Robotics Intelligence Dataset 2026},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {8729-8738},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205542},
        abstract = {This study presents a comprehensive machine learning analysis of 52 humanoid robotic platforms catalogued in the Global Humanoid Robotics Intelligence Dataset (2026), a curated cross-sectional record of the global humanoid robotics industry comprising 68 features spanning physical design, sensory architecture, AI integration, commercial maturity, and geopolitical context. We formulate three complementary analytical tasks: (1) multi-class classification of AI Maturity Score (ordinal 0–5), (2) regression of Commercial Readiness Index (continuous 0–100), and (3) unsupervised clustering to identify latent robot archetypes. Eight classifiers are benchmarked via 5-fold stratified cross-validation and Leave-One-Out (LOO) validation, appropriate for the small-N regime (N = 52). Gradient Boosting achieves the highest 5-fold weighted F1 of 0.930 ± 0.086 and a test set accuracy of 90.9%; the tuned Random Forest achieves LOO accuracy of 86.5% and LOO weighted F1 of 84.8%. For commercial readiness regression, Random Forest Regressor achieves the lowest cross-validated RMSE of 13.20 ± 3.99 with R2 = 0.615. K Means clustering on PCA-reduced features identifies six latent robot archetypes (silhouette-optimal k = 6), capturing the axis from early-stage research prototypes to commercially deployed leaders. Statistical tests confirm that geopolitical tier significantly influences AI maturity (H = 8.371, p = 0.039) and that LLM integration is associated with significantly higher commercial readiness (U = 438.0, p = 0.025). These findings provide actionable intelligence for stakeholders assessing the global humanoid robotics landscape in 2026.},
        keywords = {humanoid robotics; AI maturity classification; commercial readiness prediction; machine learning; gradient boosting; SHAP explainability; K Means clustering; geopolitical AI stratification; LLM integration},
        month = {June},
        }