Sensor-Based Contact Geometry Optimization for Multifingered Robot Hands

Abstract

This paper employs a behavior-based approach to regulate the contact geometry during a dextrous (fingertip) grasp. The goal is to provide a framework for sensor-based controllers that acquire information on-line and use this data stream to refine grasp solutions. We will present two such behaviors and illustrates how they can be used to suppress local errors in the neighborhood of a grasp solution. We present a class of objects for which our behavioral repertoire is globally competent for 2, 3, and 4 contacts, and report the performance of the grasp regulator on this class of objects. This work is part of a larger treatment for a 20 degree of freedom (DOF) eye/hand/arm system under development at the Laboratory for Perceptual Robotics. Introduction Success in dextrous manipulation tasks, as in any other real-time task involving interaction with the environment, implies considering perception and action as integral parts of the controller. Integrating perception and action in dextrous..