  Grasp planning for multi-fingered hands is computationally expensive due to
the joint-contact coupling, surface nonlinearities and high dimensionality,
thus is generally not affordable for real-time implementations. Traditional
planning methods by optimization, sampling or learning work well in planning
for parallel grippers but remain challenging for multi-fingered hands. This
paper proposes a strategy called finger splitting, to plan precision grasps for
multi-fingered hands starting from optimal parallel grasps. The finger
splitting is optimized by a dual-stage iterative optimization including a
contact point optimization (CPO) and a palm pose optimization (PPO), to
gradually split fingers and adjust both the contact points and the palm pose.
The dual-stage optimization is able to consider both the object grasp quality
and hand manipulability, address the nonlinearities and coupling, and achieve
efficient convergence within one second. Simulation results demonstrate the
effectiveness of the proposed approach. The simulation video is available at:
http://me.berkeley.edu/\%7Eyongxiangfan/IROS2018/fingersplit.html
