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.
|Number of pages
|IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
|Published - 2010 Apr
Bibliographical noteFunding Information:
Manuscript received February 13, 2009; revised May 22, 2009. First published August 18, 2009; current version published March 17, 2010. This work was supported by the Korean Institute of Construction and Transportation Technology Evaluation and Planning under Program 06-Unified and Advanced Construction Technology Program-D01. The work of J. Park was supported by the Ministry of Knowledge Economy under the Human Resources Development Program for Convergence Robot Specialists. This paper was recommended by Associate Editor A. Tayebi.
- Contact task
- Equilibrium point control
- Reinforcement learning
- Robot manipulation
ASJC Scopus subject areas
- Control and Systems Engineering
- Information Systems
- Human-Computer Interaction
- Computer Science Applications
- Electrical and Electronic Engineering