Machine learning-based motion optimization in intelligent robotic systems

Motion optimization is a critical challenge in intelligent robotic systems, directly influencing efficiency, accuracy, and energy consumption. Traditional control and trajectory planning methods often rely on predefined models and heuristics, limiting their adaptability in dynamic and uncertain environments. This paper presents a machine learning–based approach for motion optimization in intelligent robotic systems, enabling adaptive, data-driven decision making for improved performance. The proposed framework integrates supervised and reinforcement learning techniques to optimize robot trajectories, joint coordination, and actuator control in real time. Sensor data, including position, velocity, and force feedback, are utilized to continuously learn and refine motion strategies. Experimental evaluations conducted on simulated and physical robotic platforms demonstrate significant improvements in trajectory smoothness, task completion time, and energy efficiency compared to conventional control methods. The results highlight the potential of machine learning to enhance autonomy and robustness in intelligent robotic systems, making the approach suitable for applications such as medical robotics, industrial automation, and assistive technologies.