A system controller for visual servoing, comprising: a technology module (115; 120) comprising an accelerator processor (115A; 120A) for machine learning inference configured to: retrieve a desired pose configuration of a workpiece object being manipulated in its pose by a robotic gripping device; receive from one or more cameras on or near the robotic gripping device real-time visual feedback information that includes a current pose configuration of the workpiece object; and execute a machine learning model trained to process the real-time visual feedback information and determine a low dimensional pose configuration error based on a difference between the current pose configuration of the workpiece object and the desired pose configuration of the workpiece object; a pre-processing module configured to build a 3D CAD model of the workpiece object based on visual data captured by the one or more cameras, if the real-time visual feedback information indicates that the workpiece object is unknown to the system controller, wherein the machine learning model is configured to use the 3D CAD model as training input to refine the machine learning model for optimizing grasping of the workpiece object by the robotic gripping device; and a servo control module configured to adapt a servo control signal to the robotic gripping device for manipulation of the workpiece object in response to the pose configuration error; wherein the system controller is configured to iteratively repeat the process of receiving the real-time visual feedback information, determining the pose configuration error, and adapting the servo control signal to the robotic gripping device until a placement of the workpiece object substantially matches the desired pose configuration. a technology module (115; 120) comprising an accelerator processor (115A; 120A) for machine learning inference configured to: retrieve a desired pose configuration of a workpiece object being manipulated in its pose by a robotic gripping device; receive from one or more cameras on or near the robotic gripping device real-time visual feedback information that includes a current pose configuration of the workpiece object; and execute a machine learning model trained to process the real-time visual feedback information and determine a low dimensional pose configuration error based on a difference between the current pose configuration of the workpiece object and the desired pose configuration of the workpiece object; retrieve a desired pose configuration of a workpiece object being manipulated in its pose by a robotic gripping device; receive from one or more cameras on or near the robotic gripping device real-time visual feedback information that includes a current pose configuration of the workpiece object; and execute a machine learning model trained to process the real-time visual feedback information and determine a low dimensional pose configuration error based on a difference between the current pose configuration of the workpiece object and the desired pose configuration of the workpiece object; a pre-processing module configured to build a 3D CAD model of the workpiece object based on visual data captured by the one or more cameras, if the real-time visual feedback information indicates that the workpiece object is unknown to the system controller, wherein the machine learning model is configured to use the 3D CAD model as training input to refine the machine learning model for optimizing grasping of the workpiece object by the robotic gripping device; and a servo control module configured to adapt a servo control signal to the robotic gripping device for manipulation of the workpiece object in response to the pose configuration error;