AI primarily trains robots in simulation through reinforcement learning, where a robot agent learns to perform tasks by interacting with a virtual environment. In this safe and cost-effective setting, the AI system continuously explores various actions, receiving rewards or penalties based on its performance towards a defined goal. This iterative process generates vast amounts of data, which AI algorithms, often neural networks, use to optimize the robot's control policies and decision-making strategies. The simulation allows for rapid experimentation with different scenarios and failure states that would be impractical or dangerous in the real world, greatly accelerating the learning phase. Once the AI-trained robot achieves proficiency in the virtual environment, its learned knowledge and refined behaviors are then transferred to a physical robot using techniques like sim-to-real transfer. More details: https://art-by-antony.com/wordpress/wordpress/wp-content/themes/Upward/go.php?https://infoguide.com.ua