Name
Abstract | ||
|---|---|---|
A robot demonstration method is proposed based on the combination of locally weighted regression (LWR) and Q-learning algorithm. It is applied on a 6-DOF hitting-ball-system. This method can adapt to the work task by learning from demonstration and generating new actions. With the LWR algorithm, the mapping between target values and actions is established. According to deviation of landing position, a Q-learning algorithm is proposed to adjust the parameters of manipulator and compensate the errors caused by model and the controller. The model of LWR fits a local small space to approximate the global state and decision space. It turns out to reduce the dimension and simplify the training of Qlearning. The convergence rate is enhanced and the precision of performing task is improved. The simulation and experiment demonstrate the applicability of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3233/JIFS-169564 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | Field | DocType |
Reinforcement learning,Q-learning,locally weighted regression,program by demonstration | Q learning algorithm,Artificial intelligence,Robot,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
35 | 1 | 1064-1246 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guangzhe Zhao | 1 | 5 | 4.11 |
| Yong Tao | 2 | 73 | 4.63 |
| Hui Liu | 3 | 0 | 0.68 |
| Xianling Deng | 4 | 0 | 0.34 |
| Youdong Chen | 5 | 20 | 4.67 |
| Hegen Xiong | 6 | 16 | 2.28 |
| Xianwu Xie | 7 | 0 | 0.34 |
| Zengliang Fang | 8 | 0 | 0.34 |