  Manipulation tasks are sequential in nature. Grasp selection approaches that
take into account the con- straints at each task step are critical, since they
allow to both (1) Identify grasps that likely require simple arm motions
through the whole task and (2) To discard grasps that, although feasible to
achieve at earlier steps, might not be executable at later stages due to goal
task constraints. In this paper, we study how to use our previously proposed
manipulation metric for tasks in which 2 steps are required (pick-and-place and
pouring tasks). Even for such simple tasks, it was not clear how to use the
results of applying our metric (or any metric for that matter) to rank all the
candidate grasps: Should only the start state be considered, or only the goal,
or a combination of both? In order to find an answer, we evaluated the (best)
grasps selected by our metric under each of these 3 considerations. Our main
conclusion was that for tasks in which the goal state is more constrained
(pick-and-place), using a combination of the metric measured at the start and
goal states renders better performance when compared with choosing any other
candidate grasp, whereas in tasks in which the goal constraints are less
rigidly defined, the metric measured at the start state should be mainly
considered. We present quantitative results in simulation and validate our
approach's practicality with experimental results in our physical robot
manipulator, Crichton.
