  Humans coordinate the abundant degrees of freedom (DoFs) of hands to
dexterously perform tasks in everyday life. We imitate human strategies to
advance the dexterity of multi-DoF robotic hands. Specifically, we enable a
robot hand to grasp multiple objects by exploiting its kinematic redundancy,
referring to all its controllable DoFs. We propose a human-like grasp synthesis
algorithm to generate grasps using pairwise contacts on arbitrary opposing hand
surface regions, no longer limited to fingertips or hand inner surface. To
model the available space of the hand for grasp, we construct a reachability
map, consisting of reachable spaces of all finger phalanges and the palm. It
guides the formulation of a constrained optimization problem, solving for
feasible and stable grasps. We formulate an iterative process to empower
robotic hands to grasp multiple objects in sequence. Moreover, we propose a
kinematic efficiency metric and an associated strategy to facilitate exploiting
kinematic redundancy. We validated our approaches by generating grasps of
single and multiple objects using various hand surface regions. Such grasps can
be successfully replicated on a real robotic hand.
