With the development of artificial intelligence, reinforcement learning plays an increasingly important role in the robot operation filed. In this paper, a trajectory optimization method based on local dynamic model fitting is proposed to improve sample utilization and reduce the difficulty of dynamic model learning. Firstly, the Gaussian mixture model of the robot was constructed, and based on this, the accurate local dynamics model was obtained through the Normal-inverse-wishart distribution. Secondly, LQR optimization algorithm was used to optimize the robot trajectory, and the optimal control strategy was obtained during the grasping process of the robot. Finally, the effectiveness of the proposed algorithm is verified on the dynamic simulation platform. The experimental results show that the method proposed in this paper can significantly improve sample utilization and learning efficiency.


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    Title :

    Robot Trajectory Optimization with Reinforcement Learning Based on Local Dynamic Fitting


    Additional title:

    Lect.Notes Computer


    Contributors:
    Yang, Huayong (editor) / Liu, Honghai (editor) / Zou, Jun (editor) / Yin, Zhouping (editor) / Liu, Lianqing (editor) / Yang, Geng (editor) / Ouyang, Xiaoping (editor) / Wang, Zhiyong (editor) / Liang, Ji (author) / Yan, Shuo (author)

    Conference:

    International Conference on Intelligent Robotics and Applications ; 2023 ; Hangzhou, China July 05, 2023 - July 07, 2023



    Publication date :

    2023-10-16


    Size :

    13 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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