2023
Journal Articles
1.
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.
2.
Hu, Zhaoyang Jacopo; Wang, Ziwei; Huang, Yanpei; Sena, Aran; Rodriguez y Baena, Ferdinando; Burdet, Etienne
Towards Human-Robot Collaborative Surgery: Trajectory and Strategy Learning in Bimanual Peg Transfer Journal Article
In: IEEE Robotics and Automation Letters, vol. 8, no. 8, pp. 4553-4560, 2023.
Links | BibTeX | Tags: Autonomous agents, Collaboration, Human-Robot Interaction, Imitation Learning, Medical Robots, Robots, Shared Control, Surgery, Task analysis, Training, Trajectory
@article{10149474,
title = {Towards Human-Robot Collaborative Surgery: Trajectory and Strategy Learning in Bimanual Peg Transfer},
author = {Zhaoyang Jacopo Hu and Ziwei Wang and Yanpei Huang and Aran Sena and {Rodriguez y Baena}, Ferdinando and Etienne Burdet},
doi = {10.1109/LRA.2023.3285478},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {IEEE Robotics and Automation Letters},
volume = {8},
number = {8},
pages = {4553-4560},
keywords = {Autonomous agents, Collaboration, Human-Robot Interaction, Imitation Learning, Medical Robots, Robots, Shared Control, Surgery, Task analysis, Training, Trajectory},
pubstate = {published},
tppubtype = {article}
}
2021
Proceedings Articles
3.
Khoramshahi, Mahdi; Morel, Guillaume; Jarrassé, Nathanael
Intent-aware control in kinematically redundant systems: Towards collaborative wearable robots Proceedings Article
In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 10453–10460, Xi'an, China, 2021, ISSN: 2577-087X.
Abstract | Links | BibTeX | Tags: Collaboration, Costs, Ergonomics, Estimation, Kinematics, Redundancy, Wearable robots
@inproceedings{khoramshahi_intent-aware_2021-1,
title = {Intent-aware control in kinematically redundant systems: Towards collaborative wearable robots},
author = { Mahdi Khoramshahi and Guillaume Morel and Nathanael Jarrassé},
url = {https://doi.org/10.1109/ICRA48506.2021.9561351},
doi = {10.1109/ICRA48506.2021.9561351},
issn = {2577-087X},
year = {2021},
date = {2021-05-01},
urldate = {2021-05-01},
booktitle = {2021 IEEE International Conference on Robotics and Automation (ICRA)},
pages = {10453–10460},
address = {Xi'an, China},
abstract = {Many human-robot collaboration scenarios can be seen as a redundant leader-follower setup where the human (i.e., the leader) can potentially perform the task without the assistance of the robot (i.e., the follower). Thus, the goal of the collaboration, beside stable execution of the task, is to reduce the human cost; e.g., ergonomic, or cognitive cost. Such system redundancies (where the same task be achieved in different manner) can also be exploited as a communication channel for the human to convey his/her intention to the robot; since it is essential for the overall performance (both execution and assistance) that the follower recognizes the intended task in an online fashion. Having an estimation for the intended task, the robot can assist the human by reducing the human cost over the task null-space; i.e., the null-space which arises from the overall system redundancies with respect to the intended task. With the prospective of supernumerary and prosthetic robots, in this work, we primarily focus on serial manipulation in which the proximal/distal part of the kinematic chain is controlled by the leader/follower respectively. By exploiting kinematic redundancies for intention-recognition and cost-minimization, our proposed control strategy (for the follower) ensures assistance under stable execution of the task. Our results (simulations and preliminary experimentation) show the efficacy of our method in providing a seamless robotic assistance (i.e., improving human posture) toward human intended tasks (i.e., reaching motions) for wearable robotics.},
keywords = {Collaboration, Costs, Ergonomics, Estimation, Kinematics, Redundancy, Wearable robots},
pubstate = {published},
tppubtype = {inproceedings}
}
Many human-robot collaboration scenarios can be seen as a redundant leader-follower setup where the human (i.e., the leader) can potentially perform the task without the assistance of the robot (i.e., the follower). Thus, the goal of the collaboration, beside stable execution of the task, is to reduce the human cost; e.g., ergonomic, or cognitive cost. Such system redundancies (where the same task be achieved in different manner) can also be exploited as a communication channel for the human to convey his/her intention to the robot; since it is essential for the overall performance (both execution and assistance) that the follower recognizes the intended task in an online fashion. Having an estimation for the intended task, the robot can assist the human by reducing the human cost over the task null-space; i.e., the null-space which arises from the overall system redundancies with respect to the intended task. With the prospective of supernumerary and prosthetic robots, in this work, we primarily focus on serial manipulation in which the proximal/distal part of the kinematic chain is controlled by the leader/follower respectively. By exploiting kinematic redundancies for intention-recognition and cost-minimization, our proposed control strategy (for the follower) ensures assistance under stable execution of the task. Our results (simulations and preliminary experimentation) show the efficacy of our method in providing a seamless robotic assistance (i.e., improving human posture) toward human intended tasks (i.e., reaching motions) for wearable robotics.