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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.