Extracting the Orientation of a Grasped Object from Contact Information: An approach for overcoming prohibitive calibration requirements

Abstract

When manipulating a grasped object, especially with a robot hand, it is helpful to have an estimate of the object's orientation within the grasp. The object's orientation can be extracted from knowledge of the surface normals at the various points of contact with the object, but these surface normals must first be transformed into a common coordinate system. Successful execution of these transformations requires a prohibitive amount of accuracy in calibration of the arm and hand. An online method to improve these transformations in spite of calibration errors is presented. The method requires collecting contact force readings as the object is manipulated, and computing transform corrections that minimize the variation in the sum of the contact forces. Both experimental and simulated results are presented, and the implications of the results are discussed.