1. A system for optimizing a robot control program in a physics-based simulated environment using machine learning, the system including: a physically plausible virtual runtime environment to simulate a real environment for a simulated robot; a test planning and testing component operable to: define a robotic task; generate a plurality of virtual test cases for the robotic task; determine a control strategy for executing the plurality of virtual test cases and initial parameters for the control strategy; and create the physics-based simulated environment; and a robot controller operable to: based on the control strategy, execute the plurality of virtual test cases in parallel in the physics-based simulated environment; measure a success of the execution of each of the plurality of virtual test cases; store training and validation data to a historical database, the training and validation data including at least measurements of success of the execution of the plurality of virtual test cases; train a machine learning algorithm using the training and validation data; and continuously execute the plurality of virtual test cases while using the machine learning algorithm to adjust parameters of the control strategy until one or more optimal test cases are determined; wherein the robot controller is further operable to: execute the one or more optimal test cases on a physical robot; measure a success of the execution for each of the one or more optimal test cases on the physical robot in view of the robotic task; and based on the measurements of success, select at least one optimal test case from the one or more optimal test cases.