Reliable robotic grasping for uncertain objects through virtual model control

Abstract

Dexterous robotic grasping remains a central challenge in robotics due to the high-dimensional nature of multi-fingered manipulators and the complex physical interactions involved in object manipulation. This thesis investigates the application of the Virtual Mechanisms (VM) control framework to enhance grasping capabilities in anthropomorphic robotic hands. Building on the principles of passivity-based impedance control, the VM approach introduces virtual mechanical elements such as springs, dampers, and inertial components interconnected in operational space to generate intuitive and modular control behaviors. The thesis proposes two main application approaches. The first one utilizes virtual mechanisms as simple and intuitive hand-centered trajectory planners to generate human-inspired grasping motions, employing a taxonomy-based approach to implement the following common grasp types: medium wrap, power sphere, and lateral pinch. The second one extends this approach toward object-centric behavior shaping, wherein virtual mechanisms are dynamically tailored to object geometry. Both strategies are implemented and tested on a Shadow Dexterous Hand robotic platform. Results indicate that virtual mechanisms offer a robust and versatile control paradigm, enhancing grasp robustness and adaptability while preserving stability. The thesis concludes with a proof-of-concept integration of position-based feedback into the VM framework, laying the groundwork for future developments. Overall, this work highlights the potential of virtual mechanisms as a promising alternative to traditional grasp control strategies in dexterous robotic manipulation.