Abstract: A robot intelligence engine receives highly immersive virtual environment (HIVE) data characterizing a set of robot tasks executed by a test robot in a HIVE, wherein the robot tasks of the set of robot tasks include a robot skill. The robot intelligence engine receives sensor data from a problem detecting robot deployed in an environment of operation that characterizes conditions corresponding to a detected problem and searches the set of robot tasks to identify a subset of the robot tasks that are potentially employable to remedy the detected problem. The robot intelligence engine simulates the subset of robot tasks to determine a likelihood of success for the subset of robot tasks. The simulation generates a set of unsupervised robot tasks that are potentially employable to remedy the detected problem. The robot intelligence engine selects one of the subset of robot tasks or one of the unsupervised robot tasks.

Abstract: In some examples, design parameter data for an object can be received based on user input. A set of design criteria for the object can be received based on the design parameter data. A search of a learned design virtual reality (VR) database can be implemented to identify a set of candidate designs for the object based on the set of design criteria. The learned design VR database can include a plurality of previously determined designs for the object. Each candidate design for the object can be simulated in a simulation environment based on a learned design simulation database and optimization criteria to identify at least one new design for the object. A ranked design list can be generated ranking each candidate design and the at least one new design for the object based on ranking criteria.