By Topic

Bayesian grasping

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Goldberg, K.Y. ; Sch. of Comput. Sci., Carnegie Mellon, Univ., Pittsburgh, PA, USA ; Mason, Matthew T.

A Bayesian approach to the problem of autonomous manipulation in the presence of state uncertainty is described. Uncertainty is modeled with a probability distribution on the state space. Each plan (sequence of actions) defines a mapping on the state space and hence a posterior probability distribution. An attempt is made to find a plan for optimizing expected performance. The Bayesian framework is applied to a grasping problem. A planar polygon whose initial orientation is described by a uniform distribution and a frictionless parallel-jaw gripper is assumed in order to plan automatically a sequence of open-loop squeezing operations to reduce orientational uncertainty and grasp the object. Although many different performance measures are possible depending on the application, the approach is illustrated by searching for plans that optimize the robot's expected throughput

Published in:

Robotics and Automation, 1990. Proceedings., 1990 IEEE International Conference on

Date of Conference:

13-18 May 1990