Robust dexterous manipulation under object dynamics uncertainties

Dexterous manipulation has broad applications in assembly lines, warehouses and agriculture. To perform broad-scale manipulation tasks, it is desired that a multi-fingered robotic hand can robustly manipulate objects without knowing the exact objects dynamics (i.e. mass and inertia) in advance. However, realizing robust manipulation is challenging due to the complex contact dynamics, the nonlinearities of the system, and the potential sliding during manipulation. In this paper, a dual-stage grasp controller is proposed to handle these challenges. In the first stage, feedback linearization is utilized to linearize the nonlinear uncertain system. Considering the structures of uncertainties, a robust controller is designed for such a linearized system to obtain the desired Cartesian force on the object. In the second stage, a manipulation controller regulates the contact force based on the Cartesian force from the first stage. The dual-stage grasp controller is able to realize robust manipulation without contact modeling, prevent the slippage, and withstand 40% mass and 50% inertia uncertainties. Moreover, it does not require velocity measurement or 3D/6D tactile sensor. Simulation results on Mujoco verify the efficacy of the proposed method. The simulation video is available at [1].