Proprioceptive Object Shape and Size Extraction via In-Hand-Manipulation with a Variable Friction Robot Gripper

Abstract

Robotic manipulation tasks commonly rely on computer vision or tactile sensing to extract the physical characteristics of an object. However, this additional sensing capability adds complexity and financial cost to a robotic system. Our work investigates the inexpensive alternative of feature extraction via proprioceptive sensing. Our goal is to determine whether proprioceptive data combined with in-hand-manipulation provides sufficient information to enable geometric reconstruction of object profiles. We use a newly designed 3-DOF robotic gripper with variable-friction finger surfaces to perform model-free in-hand-manipulation on a set of test objects comprised of two dimensional convex prisms. We have devised a manipulation sequence based on the rotation and sliding of test objects to allow side-counting with the successful measurement of shapes and sizes with average angle and size errors of 1.64% and 6.76% respectively. In addition, we have outlined potential research directions aimed at resolving inherent limitations of proprioceptive approaches and making our algorithm generalisable to any arbitrary shape.