Abstract: A disclosed system includes a physically plausible virtual runtime environment to simulate a real-life environment for a simulated robot and a test planning and testing component to define a robotic task and generate virtual test cases for the robotic task. The test planning and testing component is further operable to generate virtual test cases for the robotic task, determine a control strategy for executing the virtual test cases, and create the physics-based simulated environment. The system further includes a robot controller operable to execute the virtual test cases in parallel in the physics-based simulated environment, measure a success of the execution, and store training and validation data to a historical database to train a machine learning algorithm. The robot controller may continuously execute the virtual test cases and use the machine learning algorithm to adjust parameters of the control strategy until optimal test cases are determined.

Abstract: A disclosed system includes a physically plausible virtual runtime environment to simulate a real-life environment for the simulated robot and a test planning and testing component operable to receive task data based used to create a plurality of plausible tests for at least one robot control program. The plurality of plausible tests is designed to execute at least one task associated with the task data. The test planning and testing component is further operable to define test parameters for each of the plurality of plausible tests. The system further includes a robot controller operable to execute the plurality of plausible tests substantially simultaneously on the simulated robot, analyze results of the execution to select an optimized robot control program from the at least one robot control program, and based on the analysis, selectively optimize the test associated with the optimized robot control program.