Click in the text-area below, and then press Enter key to start playing the video. You will be asked to press Enter again to pause the video and type-in your transcript.

{{current_subtitle}}

  • [{{time_string(subtitle.start_time)}} - {{time_string(subtitle.end_time)}}] {{subtitle.text}}

Description

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.

Questions and Answers

You need to be logged in to be able to post here.