scholarly journals A random forest application to contact-state classification for robot programming by human demonstration

2015 ◽  
Vol 32 (2) ◽  
pp. 209-227 ◽  
Author(s):  
S. Cabras ◽  
M. E. Castellanos ◽  
E. Staffetti
Keyword(s):  
Random Forest ◽  
Robot Programming ◽  
Contact State ◽  
State Classification ◽  
Human Demonstration

Remote Lead Through Teaching by Human Demonstration Device

2015 ◽  
Author(s):  
Hsien-Chung Lin ◽  
Te Tang ◽  
Masayoshi Tomizuka ◽  
Wenjie Chen
Keyword(s):  
Industrial Robots ◽  
Programming Method ◽  
Robot Programming ◽  
Production Lines ◽  
Teaching Techniques ◽  
Safe Environment ◽  
New Methods ◽  
Direct Demonstration ◽  
Programming Process ◽  
Human Demonstration

Industrial robots are playing increasingly important roles in production lines. The traditional pendant programming method, however, is unintuitive and time-consuming. Its complicated operation also sets a high requirement on users. To simplify the robot programming process, many new methods have been proposed, such as lead through teaching, teleoperation, and human direct demonstration. Each of these methods, however, suffers from its own drawbacks. To overcome the drawbacks, a novel robot programming method, remote lead through teaching (RLTT), is introduced in this paper. In RLTT, the operator uses a device to train the robot remotely, allowing the demonstrators to use the mature lead through teaching techniques in a safe environment. In order to implement RLTT, the human demonstration device (HDD) is also designed to transfer the demonstration information from the human to the robot.

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