Si, Weiyong and Wang, Ning and Yang, Chenguang (2021) Composite dynamic movement primitives based on neural networks for human–robot skill transfer. Neural Computing and Applications, 35 (32). pp. 23283-23293. DOI https://doi.org/10.1007/s00521-021-05747-8
Si, Weiyong and Wang, Ning and Yang, Chenguang (2021) Composite dynamic movement primitives based on neural networks for human–robot skill transfer. Neural Computing and Applications, 35 (32). pp. 23283-23293. DOI https://doi.org/10.1007/s00521-021-05747-8
Si, Weiyong and Wang, Ning and Yang, Chenguang (2021) Composite dynamic movement primitives based on neural networks for human–robot skill transfer. Neural Computing and Applications, 35 (32). pp. 23283-23293. DOI https://doi.org/10.1007/s00521-021-05747-8
Abstract
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 manipulation skills simultaneously for human-to-robot skills transfer. As the robot manipulator is expected to perform tasks in unstructured and uncertain environments, it requires the manipulator to own the adaptive ability to adjust its behaviours to new situations and environments. Since the DMPs can adapt to uncertainties and perturbation, and spatial and temporal scaling, it has been successfully employed for various tasks, such as trajectory planning and obstacle avoidance. However, the existing skill model mainly focuses on position or orientation modelling separately; it is a common constraint in terms of position and orientation simultaneously in practice. Besides, the generalisation of the skill learning model based on DMPs is still hard to deal with dynamic tasks, e.g., reaching a moving target and obstacle avoidance. In this paper, we proposed a composite DMPs-based framework representing position and orientation simultaneously for robot skill acquisition and the neural networks technique is used to train the skill model. The effectiveness of the proposed approach is validated by simulation and experiments.
Item Type: | Article |
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Uncontrolled Keywords: | Position and orientation skill learning framework; Composite dynamic movement primitive; Learning from demonstration; Human–robot skill transfer; Radial basis function NNs (RBFNNs) |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 11 Oct 2023 11:51 |
Last Modified: | 30 Oct 2024 21:32 |
URI: | http://repository.essex.ac.uk/id/eprint/36607 |
Available files
Filename: s00521-021-05747-8.pdf
Licence: Creative Commons: Attribution 4.0