A method to develop neural skills for a robotics system by way of a computerized engineering tool, characterized by the following steps: arranging in the computerized engineering tool a closed loop including an integrated arrangement of respective modular functionalities comprising a physics engine, a neural data editor, an experiment editor, a neural skills editor, and a machine learning environment; generating by the physics engine a plurality of data primitives indicative of 1) respective geometries of an object to be manipulated by the robotics system, and of the robotics system, 2) sensory perception of said object, and 3) one or more physical interactions by the robotics system with said object; displaying by the neural data editor an organized listing of user-selectable data elements that form the data primitives generated by the physics engine; inputting to the experiment editor one or more data sets of user-selected data elements of the organized listing of user-selectable data elements displayed by the neural data editor; preparing with the experiment editor the one or more data sets of user-selected data elements to prepare parameter data for a desired experiment case; defining with the neural skills editor a neural network, which when executed is subject to the parameter data prepared with the experiment editor; executing by the machine learning environment the defined neural network with the parameter data prepared with the experiment editor, the executing being configured to learn in the machine learning environment neural skills effective to implement at least parts of a control policy to control a sequence of tasks to be performed by a robot of the robotics system; and extracting from the machine learning environment statistical properties in connection with the neural skills being learned in the machine learning environment. arranging in the computerized engineering tool a closed loop including an integrated arrangement of respective modular functionalities comprising a physics engine, a neural data editor, an experiment editor, a neural skills editor, and a machine learning environment; generating by the physics engine a plurality of data primitives indicative of 1) respective geometries of an object to be manipulated by the robotics system, and of the robotics system, 2) sensory perception of said object, and 3) one or more physical interactions by the robotics system with said object; displaying by the neural data editor an organized listing of user-selectable data elements that form the data primitives generated by the physics engine; inputting to the experiment editor one or more data sets of user-selected data elements of the organized listing of user-selectable data elements displayed by the neural data editor; preparing with the experiment editor the one or more data sets of user-selected data elements to prepare parameter data for a desired experiment case; defining with the neural skills editor a neural network, which when executed is subject to the parameter data prepared with the experiment editor; executing by the machine learning environment the defined neural network with the parameter data prepared with the experiment editor, the executing being configured to learn in the machine learning environment neural skills effective to implement at least parts of a control policy to control a sequence of tasks to be performed by a robot of the robotics system; and extracting from the machine learning environment statistical properties in connection with the neural skills being learned in the machine learning environment.