<![CDATA[The development of robotics and machine learning technology has opened up new opportunities in the development of smarter and more adaptive robot control systems. Rumah Robot Indonesia (Robonesia) as one of the robotics innovation centers in Indonesia requires a robot control system that is capable of operating autonomously and responsive to its environment. robot that is able to operate autonomously and responsive to its environment. This research aims to develop a machine learning-based robot control system that can improve the robot's ability to perform complex tasks, such as navigation, object recognition, and interaction with the environment. The research method involves collecting data from the robot's operational environment, training a machine learning model using algorithms such as the such as Convolutional Neural Network (CNN) for object recognition and Reinforcement Learning (RL) for navigation, and testing the system in simulated and real-world scenarios. The datasets used include images, sensor data, and relevant environmental information. System performance evaluation is performed based on the metrics of object recognition accuracy, response speed, and navigation success, and navigation success. The results show that the robot control system based on machine learning-based robot control system is able to achieve object recognition accuracy of 95.2% and navigation success rate of navigation success rate of 92.8% in a dynamic environment. The system also shows rapid response to environmental changes, with an average response time of 0.8 seconds. This success demonstrates that the integration of machine learning in robot control systems can improve the robot's ability to operate autonomously. improve the robot's ability to operate autonomously and adaptively.]]>
