A computer-implemented method of providing robotic paint repair, comprising: a) receiving, by one or more processors, coordinates of each identified defect in a substrate along with characteristics of each defect; b) communicating, by the one or more processors, coordinates of an identified defect in the substrate to a robot controller module along with any additional data needed for the robot controller module to control a robot manipulator to bring an end effector of the robot manipulator into close proximity to the identified defect on the substrate; c) providing, by the one or more processors, characteristics of the defect and a current state of at least the end effector of the robot manipulator to a local or cloud-based policy server; d) receiving, by the one or more processors, a repair action from the policy server based on a previously learned control policy that is updated by a machine learning unit; e) executing, by the one or more processors, the repair action by communicating instructions to the robot controller module and end effector to implement the repair action; e1) receiving by the one or more processors, characteristics of each defect including locally collected in-situ inspection data from end effector sensors; f) providing, by the one or more processors, the in-situ data to a machine learning unit for creating learning updates using at least one of fringe pattern projection, deflectometry, and intensity measurements of diffuse reflected or normal white light using a camera; g) the one or more processors receiving quality data relating to a quality of a repair resulting from the repair action and providing the characteristics of the defect and the quality data to the policy server for logging; h) the one or more processors implementing a machine learning module that runs learning updates to improve future repair actions from the policy server based on a particular identified defect and subsequent evaluation of an executed repair. a) receiving, by one or more processors, coordinates of each identified defect in a substrate along with characteristics of each defect; b) communicating, by the one or more processors, coordinates of an identified defect in the substrate to a robot controller module along with any additional data needed for the robot controller module to control a robot manipulator to bring an end effector of the robot manipulator into close proximity to the identified defect on the substrate; c) providing, by the one or more processors, characteristics of the defect and a current state of at least the end effector of the robot manipulator to a local or cloud-based policy server; d) receiving, by the one or more processors, a repair action from the policy server based on a previously learned control policy that is updated by a machine learning unit; e) executing, by the one or more processors, the repair action by communicating instructions to the robot controller module and end effector to implement the repair action; e1) receiving by the one or more processors, characteristics of each defect including locally collected in-situ inspection data from end effector sensors; f) providing, by the one or more processors, the in-situ data to a machine learning unit for creating learning updates using at least one of fringe pattern projection, deflectometry, and intensity measurements of diffuse reflected or normal white light using a camera; g) the one or more processors receiving quality data relating to a quality of a repair resulting from the repair action and providing the characteristics of the defect and the quality data to the policy server for logging; h) the one or more processors implementing a machine learning module that runs learning updates to improve future repair actions from the policy server based on a particular identified defect and subsequent evaluation of an executed repair. <EOS>