Simulate Robot Tasks → Deploy to Hardware → Monitor Performance

Create a detailed simulation environment matching your physical workspace. Train the robot controller using reinforcement learning or imitation learning techniques to perform the target task (picking, sorting, navigation, etc.). Use domain randomization to improve real-world transfer.

Convert the trained model into a ROS node that can run on your physical robot hardware. Set up the communication bridges between simulation-trained controllers and real robot sensors/actuators. Test the deployment pipeline in a safe environment first.

Set up dashboards to monitor key performance metrics like task success rate, execution time, error rates, and sensor readings. Create alerts for when the robot encounters situations significantly different from training data.

Automatically log all robot performance metrics, sensor data, and environmental conditions. Compare real-world performance against simulation benchmarks and identify areas where the sim-to-real transfer could be improved.