Human-Inspired Object Manipulation Control with the Anatomically Correct Testbed Hand

Abstract

Dexterous manipulation with robotic hands can be achieved using object-level impedance control strategies, which allow intuitive regulation of object position, external environmental interactions, and grasp forces. However, for grasp stability, object stiffness gains are limited by the inherent compliance of the robotic system, object size/shape, and applied grasp forces, which can lead to restricted manipulation capabilities. In this work, we first use analytical modeling techniques to explore the theoretical passivity bounds on object stiffness control gains to ensure grasp stability. Then, an object-space stiffness control algorithm is developed for the Anatomically Correct Testbed (ACT) hand, a robotic hand designed to replicate the complex tendon and joint structure of the human hand, and grasp stability bounds are experimentally tested for various task scenarios. Finally, inspired by the hierarchical structure of the human neuromuscular system, we develop a novel control strategy that implements low-level stiffness in muscle-space, while also emulating a separately defined object-space stiffness in quasi-static conditions. Experimental results demonstrate that this control strategy increases achievable object stiffness without sacrificing grasp stability, leading to significantly increased manipulation capabilities.