###### Abstract  
Industrial robots are undergoing a transformative shift as Artificial Intelligence (AI)-driven and bio-inspired control strategies unlock new levels of precision, adaptability, and multi-dimensional sustainability aligned with Industry 5.0 (energy efficiency, material circularity, and life-cycle emissions). This systematic review analyzes 160 peer-reviewed industrial robotics control studies (2023-2025), including an expanded bio-inspired/human-centric subset, to evaluate: (1) the dominant and emerging control methodologies; (2) the transformative role of digital twins and 5G-enabled connectivity; and (3) the persistent technical, ethical, and environmental challenges. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, the study employs a rigorous methodology, focusing on adaptive control, deep reinforcement learning (DRL), human-robot collaboration (HRC), and quantum-inspired algorithms. The key findings highlight up to 30% latency reductions in real-time optimization, up to 22% efficiency gains through digital twins, and up to 25% energy savings from bio-inspired designs (all percentage ranges are reported relative to the comparator baselines specified in the cited sources). However, critical barriers remain, including scalability limitations (with up to 40% higher computational demands) and cybersecurity vulnerabilities (with up to 20% exposure rates). The convergence of AI, bio-inspired systems, and quantum computing is poised to enable sustainable, autonomous, and human-centric robotics, yet requires standardized safety frameworks and hybrid architectures to fully support the transition from Industry 4.0 to Industry 5.0. This review offers a strategic roadmap for future research and industrial adoption, emphasizing human-centric design, ethical frameworks, and circular-economy principles to address global manufacturing challenges. industrial robotics; adaptive control; AI-driven systems; human-robot collaboration; digital twins; Industry 5.0; quantum computing; 5G connectivity; bio-inspired control; sustainable automation; circular economy [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33]  
This review synthesizes the recent research (2023-2025) on control paradigms, enhancing precision, adaptability, and collaboration in industrial robotics; drawing on 160 peer-reviewed industrial robotics control studies (see Secton 2); and including an expanded bio-inspired/soft-robotics, human-centric subset drawn from IEEE, Scopus, and PubMed sources (Appendix A, Table A3; see Section 3.7) to provide a comprehensive snapshot. It addresses three key questions: (1) What dominant control methodologies emerge in the recent literature, and how do their performance metrics (e.g., accuracy, latency, energy use) vary across applications? (2) How do AI, digital twins, and 5G connectivity transform robot control in sectors like automotive, electronics, and logistics? (3) What technical, ethical, and environmental challenges persist, and how can they be addressed? The shift to Industry 5.0, emphasizing human-centric design, sustainability, and resilience, is also examined [1; 2; 3]. Emerging paradigms--bio-inspired control [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19], quantum-enhanced algorithms [20], cybersecurity frameworks [21; 22; 23; 24; 25; 26], and additive manufacturing (AM) integration [27; 28; 29; 30; 31; 32]--promise to overcome scalability, safety, and eco-efficiency limitations. Here, sustainability is treated broadly to include energy efficiency, material circularity, and life-cycle carbon metrics. Recent advances in adaptive control [33; 34; 35], AI-driven systems [36; 37; 38; 39; 40; 41; 42; 43; 44; 45], human-robot collaboration (HRC) [46; 47; 48; 49; 50; 51; 52; 53], and digital twins [1; 2; 54; 55; 56; 57; 58; 59; 60; 61; 62] highlight rapid progress.