Learning human compliant behavior from demonstration for force-based robot manipulation
| Autor: | Zhixian Chen, Jianwei Zhang, Lasse Einig, Cheng Zou, Jinpeng Mi, Zhen Deng |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Service robot
0209 industrial biotechnology Engineering Social robot Robot calibration business.industry Control engineering 02 engineering and technology Robot end effector Robot learning law.invention Robot control 020901 industrial engineering & automation law 0202 electrical engineering electronic engineering information engineering Trajectory Robot 020201 artificial intelligence & image processing business Simulation |
| Zdroj: | ROBIO |
| DOI: | 10.1109/robio.2016.7866342 |
| Popis: | Robot manipulation is one of prerequisites capability for service robot. However, autonomous manipulation remains a challenging problem for robot to implement the task, where robot has physical interactions and mechanical contacts with its environment. To date, learning from demonstration (LFD) has been successfully applied to enable robot to acquire new manipulation skill. Researches on LFD mainly focus on representing movement trajectory from demonstration and then transferring the new reproduced trajectory to robot. In this paper, a learning framework is introduced to learn compliant behavior from human demonstration and transferring it to robot. Multivariable, position and interaction force, will be simultaneously encoded in a probability model. The control mode of each axis in C-frame is estimated to decouple the position and force control. And an external DMP is presented to reproduce the new desired position or force for new situations. Furthermore, the compliant parameters are analyzed and estimated by combining probability modeling approach and dynamic system approach. After human compliant behavior learning, a hybrid external position/force control is presented to enable robot to produce a human-like compliant behavior. Experiments are performed to demonstrate the effectiveness of the presented learning framework. |
| Databáze: | OpenAIRE |
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