  The engineering design of robotic grippers presents an ample design space for
optimization towards robust grasping. In this paper, we adopt the
reconfigurable design of the robotic gripper using a novel soft finger
structure with omni-directional adaptation, which generates a large number of
possible gripper configurations by rearranging these fingers. Such
reconfigurable design with these omni-adaptive fingers enables us to
systematically investigate the optimal arrangement of the fingers towards
robust grasping. Furthermore, we adopt a learning-based method as the baseline
to benchmark the effectiveness of each design configuration. As a result, we
found that a 3-finger and 4-finger radial configuration is the most effective
one achieving an average 96\% grasp success rate on seen and novel objects
selected from the YCB dataset. We also discussed the influence of the
frictional surface on the finger to improve the grasp robustness.
