Quadrupedal locomotion skills are challenging to develop. In recent years, Deep Reinforcement Learning (DRL) promises to automate the development of locomotion controllers and map sensory observations to low-level actions. However, legged locomotion still is a challenging task for DRL algorithms, especially when energy efficiency is taken into consideration. In this paper, we propose a DRL scheme for efficient trotting applied on Laelaps II quadruped in MuJoCo. First, an accurate model of the robot is created by revealing the necessary parameters to be imported in the simulation, while special focus is given to the quadruped’s drivetrain. Concerning, the reward function and the action space, we investigate the best way to integrate in the reward, the terms necessary to minimize the Cost of Transport (CoT) while maintaining a trotting locomotion pattern. Last, we present how our solution increased the energy efficiency for a simple task of trotting on level terrain similar to the treadmill-robot environment at the Control Systems Lab [1] of NTUA.


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    Title :

    Learning Energy-Efficient Trotting for Legged Robots


    Additional title:

    Lect. Notes in Networks, Syst.



    Conference:

    Climbing and Walking Robots Conference ; 2022 ; Ponta Delgada, Portugal September 12, 2022 - September 14, 2022



    Publication date :

    2022-08-25


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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