We present a planning and control method that computes a minimal sequence of pokes to slide a given object from an initial pose to a desired final one (or as close to it as possible) on a planar surface. Both planning and control are based on impact models to generate pokes. Our framework takes into account the object's dynamics with a rich contact model and parameters to plan the poking sequence. The planning is conducted in the joint-space and generates trajectories tracked using an impact-aware QP control, which corrects for postpokes errors using discrete visual feedback. We implemented our method on a Panda robot arm and assessed its versatility and robustness. The experimental results show that the proposed poking approach can bring the object to the desired position and orientation with minimal errors (0.05 m for translation and 0.2 rad for rotation), highlighting its potential application in diverse industrial scenarios such as logistics.