ROBOTIC DEVICE LANGUAGE STUDY USING MACHINE LEARNING

This study explores the integration of machine learning algorithms in interpreting, developing, and optimizingrobotic device languages for autonomous communication. The research investigates how robots can be trainedto understand commands, share information, and learn languages through structured and unstructured datasets.By applying supervised and reinforcement learning methods, we aim to build a scalable linguistic modelsuitable for multi-agent robotic systems. The findings support the possibility of robots learning complexinstruction sets and executing them with minimal human intervention, which can enhance automation inindustry, health, and defense sectors.