  Dexterous manipulation has broad applications in assembly lines, warehouses
and agriculture. To perform large-scale manipulation tasks for various objects,
a multi-fingered robotic hand sometimes has to sequentially adjust its grasping
gestures, i.e. the finger gaits, to address the workspace limits and guarantee
the object stability. However, realizing finger gaits planning in dexterous
manipulation is challenging due to the complicated grasp quality metrics,
uncertainties on object shapes and dynamics (mass and moment of inertia), and
unexpected slippage under uncertain contact dynamics. In this paper, a
dual-stage optimization based planner is proposed to handle these challenges.
In the first stage, a velocity-level finger gaits planner is introduced by
combining object grasp quality with hand manipulability. The proposed finger
gaits planner is computationally efficient and realizes finger gaiting without
3D model of the object. In the second stage, a robust manipulation controller
using robust control and force optimization is proposed to address object
dynamics uncertainties and external disturbances. The dual-stage planner is
able to guarantee stability under unexpected slippage caused by uncertain
contact dynamics. Moreover, it does not require velocity measurement or
expensive 3D/6D tactile sensors. The proposed dual-stage optimization based
planner is verified by simulations on Mujoco.
