Integrating computer vision into a robotic system can provide a closed-loop controlled platform that increases the robustness of a robot's motion. This integration is also known as visual servo control or visual servoing. Visual servoing of a robot manipulator in real-time presents complex engineering problems with respect to both control and image processing particularly when we want the robot arm to perform complicated tasks such as portrait drawing. In my research, the implementation of torque feedback control and Image-based Visual Servoing (IBVS) approaches are proposed to improve previous open-loop portrait drawing tasks performed by Betty, a humanoid robot in the Autonomous Agent Lab, University of Manitoba. The implementations and evaluations of hardware, software and kinematic models are discussed in this document. I examined the problem of estimating ideal edges joining points in a pixel reduction image for an existing point-to-point portrait drawing humanoid robot, Betty. To solve this line drawing problem, two automatic sketch generators are presented. First, a modified Theta-graph, called Furthest Neighbour Theta-graph (FNTG). Second, an extension of the Edge Drawing Lines algorithm (EDLines), called Extended Edge Drawing Lines (eEDLines). The results show that the number of edges in the resulting drawing is significantly reduced without degrading the detail of the output image. The other main objective of this research is to propose the extension of the drawing robot project to further develop a robust visual servoing system for Betty to correct any drawing deviation in real-time as a human does. This is achieved by investigating and developing robust feature (lines and shading) extraction approaches for real-time feature tracking of IBVS in combination with adequate torque feedback in the drawing task.