Robots in the work place can perform hazardous or even 'impossible' tasks; e.g., toxic waste clean-up, desert and space exploration, and more. AI researchers are also interested in the intelligent processing involved in moving about and manipulating objects in the real world.
Robot manipulation is a challenging task for planning as it involves 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 for all 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 all the cups on the tray and how to grasp, move and release the cups to achieve that geometry. In the ideal case, this could be 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 full power 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 translating the symoblic predicates into geometric relationships. We then execute 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 a number of challenging manipulation problems, and demon- strate it running on a robotic platform.