Robust In-Hand Manipulation with Extrinsic Contacts

This video describes our new work on robust in-hand manipulation of objects. This work has been accepted for presentation at ICRA 2024. A brief description is provided below.
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. Unexpected contact mode transition, in the presence of unmodeled uncertainties, could be a major cause of failure in this class of tasks. Therefore, we propose a robust in-hand manipulation method, which consists of two parts. First, an in-gripper mechanics model that computes a naive 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.