ICRA 2012 Paper Abstract


Paper TuB03.5

Kehoe, Ben (University of California, Berkeley), Berenson, Dmitry (University of California, Berkeley), Goldberg, Ken (UC Berkeley)

Toward Cloud-Based Grasping with Uncertainty in Shape: Estimating Lower Bounds on Achieving Force Closure with Zero-Slip Push Grasps

Scheduled for presentation during the Regular Session "Grasp Planning" (TuB03), Tuesday, May 15, 2012, 11:30−11:45, Meeting Room 3 (Mak'to)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on December 11, 2017

Keywords Grasping, Networked Robots


This paper explores how Cloud Computing can facilitate grasping with shape uncertainty. We consider the most common robot gripper: a pair of thin parallel jaws, and a class of objects that can be modeled as extruded polygons. We model a conservative class of push-grasps that can enhance object alignment. The grasp planning algorithm takes as input an approximate object outline and Gaussian uncertainty around each vertex and center of mass. We define a grasp quality metric based on a lower bound on the probability of achieving force closure. We present a highly-parallelizable algorithm to compute this metric using Monte Carlo sampling. The algorithm uses Coulomb frictional grasp mechanics and a fast geometric test for conservative conditions for force closure. We run the algorithm on a set of sample shapes and compare the grasps with those from a planner that does not model shape uncertainty. We report computation times with single and multi-core computers and sensitivity analysis on algorithm parameters. We also describe physical grasp experiments using the Willow Garage PR2 robot.



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