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- W3207934659 abstract "Learning from demonstration (LfD) is one of the promising approaches for robots efficiently learning and regenerating human skills. Conventionally, the learning system learns both movements and stiffness profiles from human demonstrations, but rarely considers the environmental dynamic models. In this paper, we propose a human-like framework that enables the robot to learn the environmental dynamic model from a single demonstration. Within this framework, the robot will attempt to reproduce and improve the interaction skills by repetitive self-training under the task requirement. A radial basis function neural network (RBFNN) with broad learning system (BLS) is proposed to characterize the initial and updated dynamic models, and a novel motion planning method is developed to search for the optimal trajectories which minimize the changing rate of the velocity and pressing force. Then, a hybrid force/position controller is implemented to execute the interaction skills. Experimental studies have been performed on a Baxter robot to verify the effectiveness of the proposed framework." @default.
- W3207934659 created "2021-10-25" @default.
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- W3207934659 date "2021-01-01" @default.
- W3207934659 modified "2023-09-27" @default.
- W3207934659 title "A Human-Like Learning Framework of Robot Interaction Skills Based on Environmental Dynamics" @default.
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- W3207934659 doi "https://doi.org/10.1007/978-3-030-89092-6_55" @default.
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