All Outputs (14)

Incremental motor skill learning and generalization from human dynamic reactions based on dynamic movement primitives and fuzzy logic system (2021)
Journal Article
Lu, Z., Wang, N., Li, M., & Yang, C. (2022). Incremental motor skill learning and generalization from human dynamic reactions based on dynamic movement primitives and fuzzy logic system. IEEE Transactions on Fuzzy Systems, 30(6), 1506-1515. https://doi.org/10.1109/tfuzz.2021.3136933

Different from previous work on single skill learning from human demonstrations, an incremental motor skill learning, generalization and control method based on dynamic movement primitives (DMP) and broad learning system (BLS) is proposed for extract... Read More about Incremental motor skill learning and generalization from human dynamic reactions based on dynamic movement primitives and fuzzy logic system.

Iterative learning-based robotic controller with prescribed human-robot interaction force (2021)
Journal Article
Xing, X., Maqsood, K., Huang, D., Yang, C., & Li, Y. (2022). Iterative learning-based robotic controller with prescribed human-robot interaction force. IEEE Transactions on Automation Science and Engineering, 19(4), 3395-3408. https://doi.org/10.1109/tase.2021.3119400

In this article, an iterative-learning-based robotic controller is developed, which aims at providing a prescribed assistance or resistance force to the human user. In the proposed controller, the characteristic parameter of the human upper limb move... Read More about Iterative learning-based robotic controller with prescribed human-robot interaction force.

A unified parametric representation for robotic compliant skills with adaptation of impedance and force (2021)
Journal Article
Zeng, C., Li, Y., Guo, J., Huang, Z., Wang, N., & Yang, C. (2022). A unified parametric representation for robotic compliant skills with adaptation of impedance and force. IEEE/ASME Transactions on Mechatronics, 27(2), 623-633. https://doi.org/10.1109/tmech.2021.3109160

Robotic compliant manipulation is a very challenging but urgent research spot in the domain of robotics. One difficulty lies in the lack of a unified representation for encoding and learning of compliant profiles. This article aims to introduce a nov... Read More about A unified parametric representation for robotic compliant skills with adaptation of impedance and force.

Trajectory online adaption based on human motion prediction for teleoperation (2021)
Journal Article
Luo, J., Huang, D., Li, Y., & Yang, C. (2022). Trajectory online adaption based on human motion prediction for teleoperation. IEEE Transactions on Automation Science and Engineering, 19(4), 3184-3191. https://doi.org/10.1109/tase.2021.3111678

In this work, a human motion intention prediction method based on an autoregressive (AR) model for teleoperation is developed. Based on this method, the robot's motion trajectory can be updated in real time through updating the parameters of the AR m... Read More about Trajectory online adaption based on human motion prediction for teleoperation.

An improvement of robot stiffness-adaptive skill primitive generalization using the surface electromyography in human–robot collaboration (2021)
Journal Article
Guan, Y., Wang, N., & Yang, C. (2021). An improvement of robot stiffness-adaptive skill primitive generalization using the surface electromyography in human–robot collaboration. Frontiers in Neuroscience, 15, Article 694914. https://doi.org/10.3389/fnins.2021.694914

Learning from Demonstration in robotics has proved its efficiency in robot skill learning. The generalization goals of most skill expression models in real scenarios are specified by humans or associated with other perceptual data. Our proposed frame... Read More about An improvement of robot stiffness-adaptive skill primitive generalization using the surface electromyography in human–robot collaboration.

Adaptive neural control of a class of uncertain state and input-delayed systems with input magnitude and rate constraints (2021)
Journal Article
Xing, X., Liu, J., & Yang, C. (2021). Adaptive neural control of a class of uncertain state and input-delayed systems with input magnitude and rate constraints. IEEE Transactions on Systems Man and Cybernetics: Systems, 1-15. https://doi.org/10.1109/tsmc.2021.3103276

This article aims at proposing an adaptive neural control strategy for a class of nonlinear time-delay systems with input delays and unknown control directions. Different from previous researches that investigated delays and constraints separately, t... Read More about Adaptive neural control of a class of uncertain state and input-delayed systems with input magnitude and rate constraints.

Robot Learning Human Skills and Intelligent Control Design (2021)
Book
Yang, C., Zeng, C., & Zhang, J. (2021). Robot Learning Human Skills and Intelligent Control Design. CRC Press. https://doi.org/10.1201/9781003119173

In the last decades robots are expected to be of increasing intelligence to deal with a large range of tasks. Especially, robots are supposed to be able to learn manipulation skills from humans. To this end, a number of learning algorithms and techni... Read More about Robot Learning Human Skills and Intelligent Control Design.

A novel iterative identification based on the optimised topology for common state monitoring in wireless sensor networks (2021)
Journal Article
Lu, Z., Wang, N., & Yang, C. (2022). A novel iterative identification based on the optimised topology for common state monitoring in wireless sensor networks. International Journal of Systems Science, 53(1), 25-39. https://doi.org/10.1080/00207721.2021.1936275

Power consumption and data redundancy of wireless sensor networks (WSN) are widely considered for a distributed state monitoring network. For reducing the energy consumption and data amount, we propose a topology optimisation and an iterative paramet... Read More about A novel iterative identification based on the optimised topology for common state monitoring in wireless sensor networks.

Review of the techniques used in motor‐cognitive human‐robot skill transfer (2021)
Journal Article
Guan, Y., Wang, N., & Yang, C. (2021). Review of the techniques used in motor‐cognitive human‐robot skill transfer. Cognitive Computation and Systems, 3(3), 229-252. https://doi.org/10.1049/ccs2.12025

A conventional robot programming method extensively limits the reusability of skills in the developmental aspect. Engineers programme a robot in a targeted manner for the realisation of predefined skills. The low reusability of general-purpose robot... Read More about Review of the techniques used in motor‐cognitive human‐robot skill transfer.

Robot learning system based on dynamic movement primitives and neural network (2021)
Journal Article
Zhang, Y., Li, M., & Yang, C. (2021). Robot learning system based on dynamic movement primitives and neural network. Neurocomputing, 451, 205-214. https://doi.org/10.1016/j.neucom.2021.04.034

In the process of Human-robot skill transfer, we require the robot to reproduce the trajectory of teacher and expect that the robot can generalize the learned trajectory. For the trajectory after generalization, we expect that the robot arm can accur... Read More about Robot learning system based on dynamic movement primitives and neural network.

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, 3(1), 1-16. https://doi.org/10.1049/ccs2.12005

Manipulation skill learning and generalisation 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.

Distributed cooperative kinematic control of multiple robotic manipulators with improved communication efficiency (2021)
Journal Article
Zhang, J., Jin, L., & Yang, C. (2022). Distributed cooperative kinematic control of multiple robotic manipulators with improved communication efficiency. IEEE/ASME Transactions on Mechatronics, 27(1), 149 - 158. https://doi.org/10.1109/tmech.2021.3059441

An efficiency-oriented solution is theoretically a preferred choice to support the efficient operation of a system. Although some studies on the multi-manipulator system share the load of the control center by transforming the network topology, the w... Read More about Distributed cooperative kinematic control of multiple robotic manipulators with improved communication efficiency.

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, 35, 23283–23293. 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
Lu, Z., Wang, N., & Yang, C. (2021). A constrained DMPs framework for robot skills learning and generalization from human demonstrations. IEEE/ASME Transactions on Mechatronics, 26(6), 3265 - 3275. 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.