Asea Brown Boveri Ltd. (ABB) Award 2018 – Miao Li

"For his exceptional contributions to robotics, via the development of robust algorithms for dexterous manipulation of complex objects, and for the highly successful transfer of this technology to real-world industrial manipulation"

Human hand is an amazing tool, demonstrated by its incredible motor capability and remarkable sense of touch. To enable robots to work in a human-centric environment, it is desirable to endow robotic hands with human-like capabilities for grasping and object manipulation. However, due to its inherent complexity and inevitable model uncertainty, robotic grasping and manipulation remains a challenge. This thesis focuses on grasp adaptation in the face of model and sensing uncertainties: Given an object whose properties are not known with certainty (e.g., shape, weight and external perturbation), and a robotic hand, we aim at determining where to put the fingers and how the fingers should adaptively interact with the object using tactile sensing, in order to achieve either a stable grasp or a desired dynamic behaviour.

A central idea in this thesis is the object-centric dynamics: namely, that we express all control constraints into an object-centric representation. This is an essential feature that distinguishes our work from other robust grasping work in the literature, where generating a static stable grasp for a given hand is usually the primary goal. In this thesis, grasp adaptation is a dynamic process that flexibly adapts the grasp to fit some purpose from the object's perspective, in the presence of a variety of uncertainties and/or perturbations.