Darzin International Company Multidisciplinary Engineering Science Open 1 Serial Number 1 2024 02 01 Whole-Body Control for Humanoid Robots: Architectures, Optimization Back-Ends, and Benchmarking Whole-Body Control for Humanoid Robots: Architectures, Optimization Back-Ends, and Benchmarking 1 11 EN Journal Article 2023 11 25 <p class="Abstract" style="line-height: 150%;"><span style="font-size: 8.0pt; line-height: 150%;">This review aims to synthesize recent advancements in whole-body control (WBC) for humanoid robots, focusing on control architectures, optimization back-end strategies, and benchmarking methodologies that enhance stability, adaptability, and reproducibility in real-world robotic applications. A qualitative systematic review design was employed to identify and analyze contemporary trends in WBC research from 2015 to 2025. Seventeen peer-reviewed journal and conference articles were selected through targeted searches across IEEE Xplore, ScienceDirect, SpringerLink, and Scopus using keywords such as whole-body control, optimization-based control, humanoid robotics, and benchmarking frameworks. Data were analyzed thematically using NVivo 14 software through open, axial, and selective coding. The review followed an inductive interpretive approach until theoretical saturation was achieved. The synthesis process emphasized cross-comparison of architectural design features, solver types, and evaluation metrics across multiple humanoid platforms including Atlas, Talos, HRP-5P, and iCub. Five major thematic categories emerged from the qualitative synthesis: (1) control architecture design emphasizing hierarchical and modular frameworks; (2) optimization back-end strategies focusing on real-time hierarchical QP solvers, convex and non-convex formulations, and computational efficiency; (3) benchmarking and evaluation protocols aimed at reproducibility and cross-platform comparability; (4) real-time implementation challenges linked to computational latency, sensor-actuator synchronization, and fault tolerance; and (5) future research directions involving reinforcement learning integration, explainable control, and cloud-edge co-optimization. Collectively, the results highlight a clear convergence toward modular, learning-augmented, and energy-efficient WBC frameworks capable of robust real-world operation. Whole-body control research is transitioning toward hybrid optimization–learning frameworks supported by standardized benchmarking and modular software architectures. Addressing real-time constraints, safety, and interpretability will be pivotal for deploying agile, adaptive humanoid robots in human-centered environments.</span></p> https://jmesopen.com/index.php/jmesopen/article/download/25/20