Robust Dexterous Manipulation under Object Dynamics UncertaintiesYongxiang Fan, Liting Sun, Minghui Zheng, Wei Gao, Masayoshi Tomizuka Welcome! This page supplements our AIM 2017 paper submission, in which we present a dual-stage grasp controller to manipulate objects robustly under uncertainties. Dexterous manipulation has broad applications in assembly lines, warehouses and agriculture. To perform broadscale, complicated manipulation tasks, it is desired that a multifingered robotic hand can robustly manipulate objects without knowing the full dynamics of objects (i.e. mass, moment of 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 using force optimization and torque control regulates the contact force and torque 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 at least 40% mass uncertainty and 50% moment of inertia uncertainty. Moreover, it does not require velocity information of the object. Simulation results on Mujoco verify the efficacy of the proposed dual-stage grasp controller. Paper DownloadThe full paper can be downloaded through this link. Simulation ResultsVideo: Overall Demonstration Video Contactscontact my email for any questions: yongxiang_fanATberkeleyDOTedu |