## 6 Conclusions
Research & Adoption Agenda:  
(Near term, 0-2 years)--Publish open benchmarking datasets (precision/energy/latency) across standardized task suites; harmonize circular-economy KPIs (Table 2) for reporting; adopt common test harnesses for tactile & proximity safety layers in HRC; implement zero-trust + IEC 62443 security baselines in 5G pilot cells. (Mid term, 2-5 years)--Deploy hybrid AI-classical controllers that down-select inference load at the edge, targeting \(\geq\)20% compute savings; certify reusable digital-twin models for changeover validation in SMEs; establish interoperable tactile sensor protocols to close the \(\sim\)15% real-time safety gap; integrate carbon/material flow telemetry directly into scheduling optimizers. (Long term, 5+ years)--Co-design edge + quantum accelerators for real-time optimization; mature soft/bio-inspired actuators to >10\({}^{6}\) cycle durability with field-reconfigurable stiffness; codify Industry 5.0 ethics and workforce-transition metrics into international robotics standards; link life-cycle DT twins to circular-remanufacturing supply chains at scale. In closing, the path forward is clear: adaptability, sustainability, and ethics must become intrinsic design variables--not afterthoughts--in industrial robot control. By coupling data-efficient AI with physics- and safety-grounded models, extending digital twin lifecycles across energy and material flows, securing ultra-connected robot fleets, and embedding human-centric design from the outset, the robotics community can deliver resilient, sustainable, human-centered systems that meet global manufacturing demands and advance the Industry 5.0 vision. This research received no external funding. Not applicable, as this review synthesizes the existing literature without involving human subjects or experimental data, per Chilean research guidelines. Not applicable, as no human participants were involved in this literature review. Data Availability Statement: Data are contained within the article. No new experimental data were generated; this review synthesizes the existing literature available in cited references. This work was supported by the Faculty of Engineering of the Universidad de Santiago de Chile, Chile. The author declares no conflicts of interest. The following abbreviations are used in this manuscript:  
5G Fifth-Generation Mobile/Wireless Networks AI/ML Artificial Intelligence/Machine Learning AM Additive Manufacturing ANN Artificial Neural Network CPG Central Pattern Generator DoF Degrees of Freedom DT Digital Twin (virtual representation linked to a physical asset; plural: DTs) DRL Deep Reinforcement Learning EU European Union FDI Faults, Disruptions, and Interference (used in the resilient multi-robot control) FLC Fuzzy Logic Controller GAN Generative Adversarial Network HRC Human-Robot Collaboration IoT Internet of Things IIoT Industrial Internet of Things IEC International Electrotechnical Commission ISO International Organization for Standardization ISO/TS International Organization for Standardization/Technical Specification (used in ISO/TS 15066) KPI Key Performance Indicator LCA Life-Cycle Assessment LSTM Long Short-Term Memory (recurrent neural network unit) MPC Model Predictive Control MES Manufacturing Execution System PID Proportional-Integral-Derivative PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses PLC Programmable Logic Controller ROS Robot Operating System RL Reinforcement Learning SMC Sliding-Mode Control SME(s) Small- and Medium-Sized Enterprise(s)