ICRA 2012 Paper Abstract

Close

Paper WeB03.4

Dang, Hao (Columbia University), Allen, Peter (Columbia University)

Learning Grasp Stability

Scheduled for presentation during the Regular Session "Grasping: Learning and Estimation" (WeB03), Wednesday, May 16, 2012, 11:15−11:30, 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 November 18, 2017

Keywords Grasping, Force and Tactile Sensing, Contact Modelling

Abstract

We deal with the problem of blind grasping where we use tactile feedback to predict the stability of a robotic grasp given no visual or geometric information about the object being grasped. We first simulated tactile feedback using a soft finger contact model in GraspIt! and computed tactile contacts of thousands of grasps with a robotic hand using the Columbia Grasp Database. We used the K-means clustering method to learn a contact dictionary from the tactile contacts, which is a codebook that models the contact space. The feature vector for a grasp is a histogram computed based on the distribution of its contacts over the contact space defined by the dictionary. An SVM is then trained to predict the stability of a robotic grasp given this feature vector. Experiments indicate that this model which requires low-dimension feature input is useful in predicting the stability of a grasp.

 

 

Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2017 PaperCept, Inc.
Page generated 2017-11-18  19:12:19 PST  Terms of use