Name
Abstract | ||
|---|---|---|
Compared with their robotic counterparts, humans excel at various tasks by using their ability to adaptively modulate arm impedance parameters. This ability allows us to successfully perform contact tasks even in uncertain environments. This paper considers a learning strategy of motor skill for robotic contact tasks based on a human motor control theory and machine learning schemes. Our robot learning method employs impedance control based on the equilibrium point control theory and reinforcement learning to determine the impedance parameters for contact tasks. A recursive least-square filter-based episodic natural actor-critic algorithm is used to find the optimal impedance parameters. The effectiveness of the proposed method was tested through dynamic simulations of various contact tasks. The simulation results demonstrated that the proposed method optimizes the performance of the contact tasks in uncertain conditions of the environment. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TSMCB.2009.2026289 | IEEE Transactions on Systems, Man, and Cybernetics, Part B |
Keywords | Field | DocType |
optimal impedance parameter,robotic contact task,impedance control,human motor control theory,various contact task,natural actor-critic algorithm,equilibrium point control theory,modulate arm impedance parameter,impedance parameter,contact task,motor skill,adaptive modulation,robotics,testing,learning artificial intelligence,control theory,machine learning,artificial intelligence,equilibrium point,dynamic simulation,reinforcement learning,motor skills,least squares analysis,algorithms,stochastic processes,robot learning,impedance,motor control,robots | Robot learning,Impedance parameters,Computer science,Control theory,Equilibrium point,Impedance control,Artificial intelligence,Reinforcement learning,Multi-task learning,Algorithm,Motor control,Robot,Machine learning | Journal |
Volume | Issue | ISSN |
40 | 2 | 1941-0492 |
Citations | PageRank | References |
20 | 1.07 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Byungchan Kim | 1 | 44 | 7.67 |
| J Park | 2 | 1527 | 156.79 |
| Shinsuk Park | 3 | 119 | 13.60 |
| Sungchul Kang | 4 | 373 | 47.67 |