Fast and Stable Learning of Dynamical Systems Based on Extreme Learning Machine

Duan, Jin‐Chuan; Ou, Yongsheng; Hu, Jinbo; Wang, Zhiyang; Jin, Shaokun; Xu, Chao

doi:10.1109/tsmc.2017.2705279

Cited by 59 publications

(24 citation statements)

References 25 publications

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“…Programming by demonstration (PbD) is regarded as one of the most promising ways to enable robots to efficiently acquire the ability of performing tasks by transferring human dexterous manipulation skills to them [1][2][3]. Especially, for in-contact tasks where force profiles in addition to positional profiles need to be regulated [4], PbD allows relaxing the analytical burden required for the process of human-to-robot physical skills transfer [5].…”

Section: Introductionmentioning

confidence: 99%

Simultaneously Encoding Movement and sEMG-Based Stiffness for Robotic Skill Learning

Zeng

Yang

Cheng

et al. 2021

IEEE Trans. Ind. Inf.

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Section: Introductionmentioning

confidence: 99%

Simultaneously Encoding Movement and sEMG-Based Stiffness for Robotic Skill Learning

Zeng

Yang

Cheng

et al. 2021

IEEE Trans. Ind. Inf.

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“…However, these methods are not suitable for scenarios requiring high assembly accuracy and systems with compact structures. On the other hand, kinesthetic teaching and data glove teleoperation [12], [13], [14] provide an feasible method for non-expert to quickly and easily program robots. A data glove and force sensors are used to collect teaching data.…”

Section: A Motion-oriented Programming By Demonstrationmentioning

confidence: 99%

A Skill Programming Method Based on Assembly Motion Primitive for Modular Assembly System

et al. 2021

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To improve the programming efficiency of automatic assembly system, a novel skill programming framework based on task learning is proposed for modular assembly system in this paper. In this framework, the motion sequence of assembly skills can be modeled by demonstration data. And the assembly task is represented hierarchically. A complete assembly process of a part is divided into several skills, and each skill is divided into several sequential assembly motion primitives (AMP) of multiple modules. Then, a learning method of assembly motion sequence based on Hidden Markov Model is proposed, and the maximum probability method is used to generate the optimal sequential AMP. Each AMP is input to the assembly system in the form of instruction to complete the assembly. Aiming at the problem of accurate positioning and trajectory planning, visual guidance and direct teaching method are used to settle this problem. To evaluate the viability of the proposed framework, a customized modular assembly system is used to acquire the demonstration data, and a graphical user interface (GUI) software is designed. Five assembly skills are learned. Experimental are conducted to validate the effectiveness of the proposed method.

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“…The parameters of the FGMM (T i , Q i ) involve the means of the Gaussian models in the original data space, i.e., the information of the means, which is employed in GMR, is implied in (T i , Q i ) through the PCA transformation. Therefore, the regression algorithm for FGMM cannot be derived from (10) directly.…”

Section: Regression For Fgmmmentioning

confidence: 99%

Biologically Inspired Motion Modeling and Neural Control for Robot Learning From Demonstrations

Yang

Chen

Wang

et al. 2019

IEEE Trans. Cogn. Dev. Syst.

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In this paper, we propose a biologically-inspired framework for robot learning based on demonstrations. The dynamic movement primitive (DMP), which is motivated by neurobiology and human behavior, is employed to model a robotic motion that is generalizable. However, the DMP method can only be used to handle a single demonstration. To enable the robot to learn from multiple demonstrations, the DMP is combined with the Gaussian mixture model (GMM) to integrate the features of multiple demonstrations, where the conventional GMM is further replaced by the Fuzzy GMM (FGMM) to improve the fitting performance. Also, a novel regression algorithm for FGMM is derived to retrieve the nonlinear term of the DMP. Additionally, a neural network based controller is developed for the robot to track the generated motions. In this network, the cerebellar model articulation controller (CMAC) is employed to compensate for the unknown robot dynamics. The experiments have been performed on a Baxter robot to demonstrate the effectiveness of the proposed methods. Index Terms-Robot learning from demonstrations, dynamic movement primitive, fuzzy Gaussian mixture model, Gaussian mixture regression, cerebellar model articulation controller, neural control.

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Fast and Stable Learning of Dynamical Systems Based on Extreme Learning Machine

Cited by 59 publications

References 25 publications

Simultaneously Encoding Movement and sEMG-Based Stiffness for Robotic Skill Learning

Simultaneously Encoding Movement and sEMG-Based Stiffness for Robotic Skill Learning

A Skill Programming Method Based on Assembly Motion Primitive for Modular Assembly System

Biologically Inspired Motion Modeling and Neural Control for Robot Learning From Demonstrations

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