In recent years, the integration of robotics into various sectors has advanced significantly, yet the optimization of human-robot collaboration remains a crucial challenge. This study aims to improve the efficiency and safety of collaborative work environments through the development of adaptive control systems. Utilizing a blend of machine learning algorithms and real-time feedback mechanisms, we conducted experiments in simulated manufacturing settings. Our methods involved the use of reinforcement learning to adjust robot behavior dynamically in response to human actions and changing environmental conditions. The findings demonstrate that adaptive control systems can significantly reduce response times and improve task accuracy by over 30%, compared to traditional robotic systems. Conclusion drawn from this study suggests that implementing adaptive systems in robotics can greatly enhance cooperative tasks between humans and machines, pointing towards a future where seamless interaction is achievable. Further research is encouraged to explore additional applications and validate these findings in real-world environments.