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.
| Original language | English |
|---|---|
| Article number | 5204203 |
| Pages (from-to) | 433-443 |
| Number of pages | 11 |
| Journal | IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics |
| Volume | 40 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2010 Apr 1 |
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Keywords
- Contact task
- Equilibrium point control
- Reinforcement learning
- Robot manipulation
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Computer Science Applications
- Human-Computer Interaction
- Information Systems
- Software
- Medicine(all)
Cite this
Impedance learning for robotic contact tasks using natural actor-critic algorithm. / Kim, Byungchan; Park, Jooyoung; Park, Shin Suk; Kang, Sungchul.
In: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 40, No. 2, 5204203, 01.04.2010, p. 433-443.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Impedance learning for robotic contact tasks using natural actor-critic algorithm
AU - Kim, Byungchan
AU - Park, Jooyoung
AU - Park, Shin Suk
AU - Kang, Sungchul
PY - 2010/4/1
Y1 - 2010/4/1
N2 - 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.
AB - 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.
KW - Contact task
KW - Equilibrium point control
KW - Reinforcement learning
KW - Robot manipulation
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UR - http://www.scopus.com/inward/citedby.url?scp=77949776001&partnerID=8YFLogxK
U2 - 10.1109/TSMCB.2009.2026289
DO - 10.1109/TSMCB.2009.2026289
M3 - Article
VL - 40
SP - 433
EP - 443
JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
SN - 1083-4419
IS - 2
M1 - 5204203
ER -