The combination of task and motion planning presents us with a new problem that we call geometric backtracking. This problem arises from the fact that a single symbolic state or action can be geometrically instantiated in infinitely many ways. When a symbolic action cannot begeometrically validated, we may need to backtrack in thespace of geometric configurations, which greatly increases thecomplexity of the whole planning process. In this paper, weaddress this problem using intervals to represent geometricconfigurations, and constraint propagation techniques to shrinkthese intervals according to the geometric constraints of the problem. After propagation, either (i) the intervals are shrunk, thus reducing the search space in which geometric backtracking may occur, or (ii) the constraints are inconsistent, indicating then infeasibility of the sequence of actions without further effort. We illustrate our approach on scenarios in which a two-arm robot manipulates a set of objects, and report experiments that show how the search space is reduced.