2023
Wang, Ziwei; Fei, Haolin; Huang, Yanpei; Rouxel, Quentin; Xiao, Bo; Li, Zhibin; Burdet, Etienne
Learning to Assist Bimanual Teleoperation using Interval Type-2 Polynomial Fuzzy Inference Journal Article
In: IEEE Transactions on Cognitive and Developmental Systems, pp. 1–1, 2023, ISSN: 2379-8939, (Conference Name: IEEE Transactions on Cognitive and Developmental Systems).
Abstract | Links | BibTeX | Tags: Bimanual manipulation, Collaboration, Fuzzy sets, Gaussian process, Human-robot collaboration, IT2 polynomial fuzzy system, Robot kinematics, Robot learning, Robots, Task analysis, Trajectory, Uncertainty
@article{wang_learning_2023,
title = {Learning to Assist Bimanual Teleoperation using Interval Type-2 Polynomial Fuzzy Inference},
author = { Ziwei Wang and Haolin Fei and Yanpei Huang and Quentin Rouxel and Bo Xiao and Zhibin Li and Etienne Burdet},
doi = {10.1109/TCDS.2023.3272730},
issn = {2379-8939},
year = {2023},
date = {2023-01-01},
journal = {IEEE Transactions on Cognitive and Developmental Systems},
pages = {1–1},
abstract = {Assisting humans in collaborative tasks is a promising application for robots, however effective assistance remains challenging. In this paper, we propose a method for providing intuitive robotic assistance based on learning from human natural limb coordination. To encode coupling between multiple-limb motions, we use a novel interval type-2 (IT2) polynomial fuzzy inference for modeling trajectory adaptation. The associated polynomial coefficients are estimated using a modified recursive least-square with a dynamic forgetting factor. We propose to employ a Gaussian process to produce robust human motion predictions, and thus address the uncertainty and measurement noise of the system caused by interactive environments. Experimental results on two types of interaction tasks demonstrate the effectiveness of this approach, which achieves high accuracy in predicting assistive limb motion and enables humans to perform bimanual tasks using only one limb.},
note = {Conference Name: IEEE Transactions on Cognitive and Developmental Systems},
keywords = {Bimanual manipulation, Collaboration, Fuzzy sets, Gaussian process, Human-robot collaboration, IT2 polynomial fuzzy system, Robot kinematics, Robot learning, Robots, Task analysis, Trajectory, Uncertainty},
pubstate = {published},
tppubtype = {article}
}
Assisting humans in collaborative tasks is a promising application for robots, however effective assistance remains challenging. In this paper, we propose a method for providing intuitive robotic assistance based on learning from human natural limb coordination. To encode coupling between multiple-limb motions, we use a novel interval type-2 (IT2) polynomial fuzzy inference for modeling trajectory adaptation. The associated polynomial coefficients are estimated using a modified recursive least-square with a dynamic forgetting factor. We propose to employ a Gaussian process to produce robust human motion predictions, and thus address the uncertainty and measurement noise of the system caused by interactive environments. Experimental results on two types of interaction tasks demonstrate the effectiveness of this approach, which achieves high accuracy in predicting assistive limb motion and enables humans to perform bimanual tasks using only one limb.