Task‐space control is an important job in robotics and robot control, where the robot end effector is forced to reach the desired reference position and orientation in the workspace. Reinforcement learning (RL) has been used for robot control based on model‐free and online algorithms. RL methods for robot control include three subclasses: value iteration, policy iteration, and policy search. This chapter discusses the classical joint and task space control schemes, which give the theory and the first solution of the inverse and velocity kinematics problems. It provides the RL approach, which is inspired in the classical solutions. RL for robot control can be regarded as a Markov decision process. The chapter compares multi‐agent RL with the following classical methods: the Jacobian‐based augmented method, the Jacobian‐based singularity avoidance, the Jacobian‐based singular‐value‐decomposition, and the multilayer neural networks. The curse of dimensionality in RL and controllability problems for redundant robot control are overcome.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Redundant Robots Control Using Multi‐Agent Reinforcement Learning


    Beteiligte:
    Yu, Wen (Autor:in) / Perrusquía, Adolfo (Autor:in)


    Erscheinungsdatum :

    05.10.2021


    Format / Umfang :

    19 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Redundant Robots

    Maciejewski, Anthony A. / Xie, Biyun | Springer Verlag | 2025




    Obstacle avoidance for redundant robots using configuration control

    Seraji, Homayoun / Colbaugh, Richard D. / Glass, Kristin L. | NTRS | 1992


    Research on Multi-Agent Reinforcement Learning Traffic Control

    Fu, Xinpeng / Chen, Simin / Liang, Qixian et al. | IEEE | 2023