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Paper TuD04.4

Lin, Yun (University of South florida), Ren, Shaogang (University of South Florida), Clevenger, Matthew (University of South Florida), Sun, Yu (University of South Florida)

Learning Grasping Force from Demonstration

Scheduled for presentation during the Regular Session "Force, Torque and Contacts in Grasping and Assembly" (TuD04), Tuesday, May 15, 2012, 17:15−17:30, Meeting Room 4 (Chief Wabasha)

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 August 21, 2018

Keywords Grasping, Learning and Adaptive Systems, Dexterous Manipulation

Abstract

This paper presents a novel force learning framework to learn fingertip force for a grasping and manipulation process from a human teacher with a force imaging approach. A demonstration station is designed to measure fingertip force without attaching force sensor on fingertips or objects so that this approach can be used with daily living objects. A Gaussian Mixture Model (GMM) based machine learning approach is applied on the fingertip force and position to obtain the motion and force model. Then a force and motion trajectory is generated with Gaussian Mixture Regression (GMR) from the learning result. The force and motion trajectory is applied to a robotic arm and hand to carry out a grasping and manipulation task. An experiment was designed and carried out to verify the learning framework by teaching a Fanuc robotic arm and a BarrettHand a pick-and-place task with demonstration. Experimental results show that the robot applied proper motions and forces in the pick-and-place task from the learned model.

 

 

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