  We present in-hand manipulation tasks where a robot moves an object in grasp,
maintains its external contact mode with the environment, and adjusts its
in-hand pose simultaneously. The proposed manipulation task leads to complex
contact interactions which can be very susceptible to uncertainties in
kinematic and physical parameters. Therefore, we propose a robust in-hand
manipulation method, which consists of two parts. First, an in-gripper
mechanics model that computes a na\"ive motion cone assuming all parameters are
precise. Then, a robust planning method refines the motion cone to maintain
desired contact mode regardless of parametric errors. Real-world experiments
were conducted to illustrate the accuracy of the mechanics model and the
effectiveness of the robust planning framework in the presence of kinematics
parameter errors.
