Robot manipulation is a challenging task for planning as itinvolves a mixture of symbolic planning and geometric plan-ning. We would like to express goals and many action ef-fects symbolically, for example specifying a goal such as forall x, if x is a cup, then x should be on the tray, but to ac-complish this we may need to plan the geometry of fitting allthe cups on the tray and how to grasp, move and release thecups to achieve that geometry. In the ideal case, this couldbe accomplished by a fully hybrid planner that alternates be-tween geometric and symbolic reasoning to generate a solu-tion. However, in practice this is very complex, and the fullpower of this approach may only be required for a small sub-set of problems. Instead, we plan completely symbolically,and then attempt to generate a geometric plan by translatingthe symoblic predicates into geometric relationships. We thenexecute this plan in simulation, and if it fails, we backtrack,first in geometric space, and then if necessary in symbolic.We show that this approach, while not complete, solves anumber of challenging manipulation problems, and demon-strate it running on a robotic platform.