Learning Grasping Force from Demonstration

Document Type

Conference Proceeding

Publication Date



robot vision, dexterous manipulators, force measurement, Gaussian processes, learning (artificial intelligence), regression analysis, learning-from-demonstration, grasping force learning framework, fingertip force learning, grasping process, manipulation process, force imaging, fingertip force measurement, Gaussian mixture model based machine learning, GMM, motion model, force model, force trajectory generation, motion trajectory generation, Gaussian mixture regression, robotic arm, Fanuc robotic arm, BarrettHand, pick-and-place task

Digital Object Identifier (DOI)



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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Citation / Publisher Attribution

2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, 2012, p. 1526-1531.