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A review on manipulation skill acquisition through teleoperation-based learning from demonstration (2021)
Journal Article
Si, W., Wang, N., & Yang, C. (2021). A review on manipulation skill acquisition through teleoperation-based learning from demonstration. Cognitive Computation and Systems, 1-16. https://doi.org/10.1049/ccs2.12005

Manipulation skill learning and generalization have gained increasing attention due to the wide applications of robot manipulators and the spurt of robot learning techniques. Especially, the learning from demonstration method has been exploited widel... Read More about A review on manipulation skill acquisition through teleoperation-based learning from demonstration.

Composite dynamic movement primitives based on neural networks for human–robot skill transfer (2021)
Journal Article
Si, W., Wang, N., & Yang, C. (in press). Composite dynamic movement primitives based on neural networks for human–robot skill transfer. Neural Computing and Applications, https://doi.org/10.1007/s00521-021-05747-8

In this paper, composite dynamic movement primitives (DMPs) based on radial basis function neural networks (RBFNNs) are investigated for robots’ skill learning from human demonstrations. The composite DMPs could encode the position and orientation ma... Read More about Composite dynamic movement primitives based on neural networks for human–robot skill transfer.

A constrained DMPs framework for robot skills learning and generalization from human demonstrations (2021)
Journal Article
Wang, N., Lu, Z., & Yang, C. (in press). A constrained DMPs framework for robot skills learning and generalization from human demonstrations. IEEE/ASME Transactions on Mechatronics, https://doi.org/10.1109/TMECH.2021.3057022

Dynamical movement primitives (DMPs) model is a useful tool for efficiently robotic learning manipulation skills from human demonstrations and then generalizing these skills to fulfill new tasks. It is improved and applied for the cases with multiple... Read More about A constrained DMPs framework for robot skills learning and generalization from human demonstrations.

Learning compliant robotic movements based on biomimetic motor adaptation (2020)
Journal Article
Zeng, C., Chen, X., Wang, N., & Yang, C. (2021). Learning compliant robotic movements based on biomimetic motor adaptation. Robotics and Autonomous Systems, 135, https://doi.org/10.1016/j.robot.2020.103668

It is one of the great challenges for a robot to learn compliant movements in interaction tasks. The robot can easily acquire motion skills from a human tutor by kinematics demonstration, however, this becomes much more difficult when it comes to the... Read More about Learning compliant robotic movements based on biomimetic motor adaptation.

An incremental learning framework to enhance teaching by demonstration based on multimodal sensor fusion (2020)
Journal Article
Li, J., Zhong, J., Yang, J., & Yang, C. (2020). An incremental learning framework to enhance teaching by demonstration based on multimodal sensor fusion. Frontiers in Neurorobotics, 14(55), https://doi.org/10.3389/fnbot.2020.00055

Though a robot can reproduce the demonstration trajectory from a human demonstrator by teleoperation, there is a certain error between the reproduced trajectory and the desired trajectory. To minimize this error, we propose a multimodal incremental l... Read More about An incremental learning framework to enhance teaching by demonstration based on multimodal sensor fusion.

Disturbance observer enhanced variable gain controller for robot teleoperation with motion capture using wearable armbands (2020)
Journal Article
Huang, D., Yang, C., Ju, Z., & Dai, S. (2020). Disturbance observer enhanced variable gain controller for robot teleoperation with motion capture using wearable armbands. Autonomous Robots, 44(7), 1217-1231. https://doi.org/10.1007/s10514-020-09928-7

Disturbance observer (DOB) based controller performs well in estimating and compensating for perturbation when the external or internal unknown disturbance is slowly time varying. However, to some extent, robot manipulators usually work in complex en... Read More about Disturbance observer enhanced variable gain controller for robot teleoperation with motion capture using wearable armbands.

Novel joint-drift-free scheme at acceleration level for robotic redundancy resolution with tracking error theoretically eliminated (2020)
Journal Article
Jin, L., Xie, Z., Liu, M., Chen, K., Li, C., & Yang, C. (2021). Novel joint-drift-free scheme at acceleration level for robotic redundancy resolution with tracking error theoretically eliminated. IEEE/ASME Transactions on Mechatronics, 26(1), 90-101. https://doi.org/10.1109/tmech.2020.3001624

In this article, three acceleration-level joint-drift-free (ALJDF) schemes for kinematic control of redundant manipulators are proposed and analyzed from perspectives of dynamics and kinematics with the corresponding tracking error analyses. First, t... Read More about Novel joint-drift-free scheme at acceleration level for robotic redundancy resolution with tracking error theoretically eliminated.

Neural-learning-based force sensorless admittance control for robots with input deadzone (2020)
Journal Article
Peng, G., Chen, C. L. P., He, W., & Yang, C. (2021). Neural-learning-based force sensorless admittance control for robots with input deadzone. IEEE Transactions on Industrial Electronics, 68(6), 5184-5196. https://doi.org/10.1109/tie.2020.2991929

This paper presents a neural networks based admittance control scheme for robotic manipulators when interacting with the unknown environment in the presence of the actuator deadzone without needing force sensing. A compliant behaviour of robotic mani... Read More about Neural-learning-based force sensorless admittance control for robots with input deadzone.

Adaptive dynamic programming-based controller with admittance adaptation for robot–environment interaction (2020)
Journal Article
Zhan, H., Huang, D., Chen, Z., Wang, M., & Yang, C. (2020). Adaptive dynamic programming-based controller with admittance adaptation for robot–environment interaction. International Journal of Advanced Robotic Systems, 17(3), 172988142092461. https://doi.org/10.1177/1729881420924610

The problem of optimal tracking control for robot–environment interaction is studied in this article. The environment is regarded as a linear system and an admittance control with iterative linear quadratic regulator method is obtained to guarantee t... Read More about Adaptive dynamic programming-based controller with admittance adaptation for robot–environment interaction.

Biologically inspired deadbeat control of robotic leg prostheses (2020)
Journal Article
Pi, M., Li, Z., Li, Q., Kan, Z., Xu, C., Kang, Y., …Yang, C. (2020). Biologically inspired deadbeat control of robotic leg prostheses. IEEE/ASME Transactions on Mechatronics, 25(6), 2733-2742. https://doi.org/10.1109/tmech.2020.2990406

Recent advances in robotics technology provide great support for robotic leg prostheses to realize full biomechanical functionalities of the contralateral leg. In order to reproduce the biomechanical behaviors of the contralateral leg, this article a... Read More about Biologically inspired deadbeat control of robotic leg prostheses.

Multi-hierarchy interaction control of a redundant robot using impedance learning (2020)
Journal Article
Jiang, Y., Yang, C., Wang, Y., Ju, Z., Li, Y., & Su, C. (2020). Multi-hierarchy interaction control of a redundant robot using impedance learning. Mechatronics, 67, https://doi.org/10.1016/j.mechatronics.2020.102348

The control of robots with a compliant joint motion is important for reducing collision forces and improving safety during human robot interactions. In this paper, a multi-hierarchy control framework is proposed for the redundant robot to enable the... Read More about Multi-hierarchy interaction control of a redundant robot using impedance learning.

Admittance-based controller design for physical human-robot interaction in the constrained task space (2020)
Journal Article
He, W., Xue, C., Yu, X., Li, Z., & Yang, C. (2020). Admittance-based controller design for physical human-robot interaction in the constrained task space. IEEE Transactions on Automation Science and Engineering, 17(4), 1937-1949. https://doi.org/10.1109/tase.2020.2983225

In this article, an admittance-based controller for physical human-robot interaction (pHRI) is presented to perform the coordinated operation in the constrained task space. An admittance model and a soft saturation function are employed to generate a... Read More about Admittance-based controller design for physical human-robot interaction in the constrained task space.

A method of motion recognition based on electromyographic signals (2020)
Journal Article
Luo, J., Liu, C., Feng, Y., & Yang, C. (2020). A method of motion recognition based on electromyographic signals. Advanced Robotics, 34(15), 976-984. https://doi.org/10.1080/01691864.2020.1750480

In a robot-assisted surgery, a skillful surgeon can perform the operation excellently through flexible wrist motions and rich experience. However, there are little researches about the relationship between the wrist motion and electromyography (EMG)... Read More about A method of motion recognition based on electromyographic signals.

Hand gesture recognition in complex background based on convolutional pose machine and fuzzy gaussian mixture models (2020)
Journal Article
Zhang, T., Lin, H., Ju, Z., & Yang, C. (2020). Hand gesture recognition in complex background based on convolutional pose machine and fuzzy gaussian mixture models. International Journal of Fuzzy Systems, 22, 1330-1341. https://doi.org/10.1007/s40815-020-00825-w

© 2020, The Author(s). Hand gesture is one of the most intuitive and natural ways for human to communicate with computers, and it has been widely adopted in many human–computer interaction applications. However, it is still a challenging problem when... Read More about Hand gesture recognition in complex background based on convolutional pose machine and fuzzy gaussian mixture models.

Reinforcement learning control of a flexible two-link manipulator: an experimental investigation (2020)
Journal Article
He, W., Gao, H., Zhou, C., Yang, C., & Li, Z. (2020). Reinforcement learning control of a flexible two-link manipulator: an experimental investigation. IEEE Transactions on Systems Man and Cybernetics: Systems, 1-11. https://doi.org/10.1109/tsmc.2020.2975232

This article discusses the control design and experiment validation of a flexible two-link manipulator (FTLM) system represented by ordinary differential equations (ODEs). A reinforcement learning (RL) control strategy is developed that is based on a... Read More about Reinforcement learning control of a flexible two-link manipulator: an experimental investigation.

Information entropy-based intention prediction of aerial targets under uncertain and incomplete information (2020)
Journal Article
Zhou, T., Chen, M., Wang, Y., He, J., & Yang, C. (2020). Information entropy-based intention prediction of aerial targets under uncertain and incomplete information. Entropy, 22(3), https://doi.org/10.3390/e22030279

© 2020 by authors. To improve the effectiveness of air combat decision-making systems, target intention has been extensively studied. In general, aerial target intention is composed of attack, surveillance, penetration, feint, defense, reconnaissance... Read More about Information entropy-based intention prediction of aerial targets under uncertain and incomplete information.

Mixed reality enhanced user interactive path planning for omnidirectional mobile robot (2020)
Journal Article
Wu, M., Dai, S., & Yang, C. (2020). Mixed reality enhanced user interactive path planning for omnidirectional mobile robot. Applied Sciences, 10(3), https://doi.org/10.3390/app10031135

This paper proposes a novel control system for the path planning of an omnidirectional mobile robot based on mixed reality. Most research on mobile robots is carried out in a completely real environment or a completely virtual environment. However, a... Read More about Mixed reality enhanced user interactive path planning for omnidirectional mobile robot.

A sEMG-based shared control system with no-target obstacle avoidance for omnidirectional mobile robots (2020)
Journal Article
Kong, H., Yang, C., Li, G., & Dai, S. (2020). A sEMG-based shared control system with no-target obstacle avoidance for omnidirectional mobile robots. IEEE Access, 8, 26030-26040. https://doi.org/10.1109/access.2020.2970468

We propose a novel shared control strategy for mobile robots in a human-robot interaction manner based on surface eletromyography (sEMG) signals. For security reasons, an obstacle avoidance scheme is introduced to the shared control system as collisi... Read More about A sEMG-based shared control system with no-target obstacle avoidance for omnidirectional mobile robots.