In this section
Below is a list of all relevant publications authored by Robotics Forum members.
@article{Tsai:2020:10.1109/LRA.2020.2965392,
author = {Tsai, Y-Y and Xiao, B and Johns, E and Yang, G-Z},
doi = {10.1109/LRA.2020.2965392},
journal = {IEEE Robotics and Automation Letters},
pages = {683--690},
title = {Constrained-space optimization and reinforcement learning for complex tasks},
url = {http://dx.doi.org/10.1109/LRA.2020.2965392},
volume = {5},
year = {2020}
}
TY - JOUR
AB - Learning from demonstration is increasingly used for transferring operator manipulation skills to robots. In practice, it is important to cater for limited data and imperfect human demonstrations, as well as underlying safety constraints. This article presents a constrained-space optimization and reinforcement learning scheme for managing complex tasks. Through interactions within the constrained space, the reinforcement learning agent is trained to optimize the manipulation skills according to a defined reward function. After learning, the optimal policy is derived from the well-trained reinforcement learning agent, which is then implemented to guide the robot to conduct tasks that are similar to the experts' demonstrations. The effectiveness of the proposed method is verified with a robotic suturing task, demonstrating that the learned policy outperformed the experts' demonstrations in terms of the smoothness of the joint motion and end-effector trajectories, as well as the overall task completion time.
AU - Tsai,Y-Y
AU - Xiao,B
AU - Johns,E
AU - Yang,G-Z
DO - 10.1109/LRA.2020.2965392
EP - 690
PY - 2020///
SN - 2377-3766
SP - 683
TI - Constrained-space optimization and reinforcement learning for complex tasks
T2 - IEEE Robotics and Automation Letters
UR - http://dx.doi.org/10.1109/LRA.2020.2965392
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000526519900006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - https://ieeexplore.ieee.org/document/8954748
VL - 5
ER -
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