Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICRA.2012.6225222 | 2012 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) |
Keywords | Field | DocType |
regression analysis,gmm,machine learning,robots,calibration,robot arm,gaussian mixture model,robotic arm,force sensor,gaussian processes,force,learning artificial intelligence | Robotic arm,Daily living,Control engineering,Artificial intelligence,Gaussian process,Trajectory,Force sensor,Computer vision,Simulation,Gaussian mixture regression,Engineering,Robot,Mixture model | Conference |
Volume | Issue | ISSN |
2012 | 1 | 1050-4729 |
Citations | PageRank | References |
4 | 0.42 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yun Lin | 1 | 15 | 1.54 |
| Shaogang Ren | 2 | 26 | 3.43 |
| Matthew Clevenger | 3 | 13 | 1.63 |
| Yu Sun | 4 | 208 | 35.82 |