Low-Dimensional Robotic Grasping: Eigengrasp Subspaces and Optimized Underactuation

Abstract

This thesis introduces new methods for enabling the effective use of highly dexterous robotic hands, interfacing with the upcoming generation of neurally controlled hand prostheses, and designing a new class of simple yet effective grasping devices based on underactuation and mechanical adaptation. These methods share a common goal: reducing the complexity that has traditionally been associated, at both computational and mechanical levels, with robotic grasping in unstructured environments. A key prerequisite for robot operation in human settings is versatility, which, in terms of autonomous grasping, translates into the ability to reliably acquire and interact with a wide range of objects. In an attempt to match the abilities of the most versatile end-effector known, the human hand, many anthropomorphic robotic models have been proposed, with the number of degrees of freedom starting to approach that of their human counterpart. However, these models have proven difficult to use in practice, as the high dimensionality of the posture space means that finding adequate grasps for a target object is often an intractable problem. In this thesis, we propose using low-dimensional posture subspaces for dexterous or