Robotic Grasp Optimization from Contact Force Analysis

Filipe Fernandes

Key information

Authors:

Filipe Fernandes (Filipe Fernandes Veiga)

Supervisors:

José Alberto Rosado dos Santos Victor (José Santos Victor); Alexandre José Malheiro Bernardino (Alexandre José Malheiro Bernardino)

Published in

06/22/2012

Abstract

This thesis addresses the problem of robotic grasp optimization. Due to uncertainty both on the robot kinematics, control and perception, it is very hard to analytically compute good grasps and execute them successfully. Our approach is based on searching for the best grasp configurations by iteratively optimizing a suitable grasp criterion. The thesis is composed by two main parts. On the first part we study the problem of grasp quality assessment. We propose a grasp quality metric that provides coherent performance criteria even for non-force-Closure grasps. It is based in two improvements with respect to existing metrics: a surface contact model, providing smoothness properties, and a bimodal wrench space analysis that provides continuity in the transition from non-force-closure to force-closure grasps. It is experimentally demonstrated, in a series of simulations, that the developed metric outperforms existing ones. On the second part of the thesis we study the problem of efficiently searching for the best grasp. Naive optimization of the grasp quality metric (e.g. uniform sampling) becomes intractable very quickly as the search space parameters grow. To address this problem we apply recent methods on Bayesian Optimization to obtain good approximations to global optima in a reduced number of trials. Gaussian Processes are used to encode our knowledge and uncertainty of the grasp quality metric as new trials are performed. Then, new samples are planned by searching in the Gaussian Process for points with favorable expected improvement. This last search is performed using recent Global Optimization methods.