Robust grasp preimages under unknown mass and friction distributions

Issue title: Enabling Robot Autnomy

Guest editors: Craig Schlenoff, Stephen Balakirsky and Henrik Christensen

Article type: Research Article

Authors: Price, Andrew^{a; *} | Balakirsky, Stephen^b | Christensen, Henrik^c

Affiliations: [a] Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, USA | [b] Robotics and Autonomous Systems Division, Georgia Tech Research Institute, Atlanta, GA, USA | [c] Department of Computer Science and Engineering, University of California, San Diego, CA, USA

Correspondence: [*] Corresponding author: Andrew Price, Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, 801 Atlantic Dr NW, Atlanta, GA 30332, USA. E-mail: [email protected].

Abstract: This work introduces an algorithm for the computation of robust grasp preimages: the space of initial poses from which an object will converge into the desired grasp. Building on existing motion and friction models for pushed objects under contact, we describe a game-theoretic technique for estimating worst-case scenarios for difficult to observe properties like pressure and friction distributions. The use of this antagonistic model in the grasping simulations provides for a conservative estimate of the preimage of the given grasp. The antagonistic model is then validated against data from real grasping experiments on various robot grippers.

Keywords: Grasping, grasp planning, differential games, motion planning, sliding

DOI: 10.3233/ICA-180568

Journal: Integrated Computer-Aided Engineering, vol. 25, no. 2, pp. 99-110, 2018