Vision-based precision manipulation with underactuated hands: Simple and effective solutions for dexterity

Abstract

In this paper, a method is proposed for vision-based within-hand precision manipulation with underactuated grippers. The method combines the advantages of adaptive underactuation with the robustness of visual servoing algorithms by employing simple action sets in actuator space, called precision manipulation primitives (PMPs). It is shown that, with this approach, reliable precision manipulation is possible even without joint and force sensors by using only minimal gripper kinematics information. An adaptation method is also utilized in the vision loop to enhance the system's transient performance. The proposed methods are analyzed with experiments using various target objects and reference signals. The results indicate that underactuated hands, even with minimalistic sensing and control via visual servoing, can provide a simple and inexpensive solution to allow low-fidelity precision manipulation.