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• W4387394711 endingPage "104550" @default.
• W4387394711 startingPage "104550" @default.
• W4387394711 abstract "Quadruped robots have attracted many researchers due to their unique advantages. In the field of control, deep reinforcement learning (DRL) saves the complex and tedious design of traditional control algorithms and motivates the robot to learn the motion patterns by itself, which is a promising alternative approach. However, due to the lack of prior information that is difficult to obtain in the physical world, policy usually exhibits a single motion state when faced with multiple scenarios. In this paper, we adopt a teacher-student policy architecture that uses the student policy to implicitly identify the teacher policy containing a privileged information, enabling the robot to adjust its motion depending on the environment it is in. We propose the synthetic terrain-command curriculum that assigns different levels of command curriculum depending on the difficulty of the terrain, which is useful for improving policy adaptation and stability. Policy is often trapped in local optima due to the nonlinearity of the robot system and unreasonable parameters. We propose a self-correction mechanism to jump out of this state in time, so that the policy can be steadily improved. We demonstrate our policy on the real quadruped platform named SDUQuad-48, and the results show that the learned policy exhibits state-of-the-art performance in both high-speed locomotion and complex terrain adaptation." @default.
• W4387394711 created "2023-10-07" @default.
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• W4387394711 date "2023-12-01" @default.
• W4387394711 modified "2023-10-18" @default.
• W4387394711 title "Stable skill improvement of quadruped robot based on privileged information and curriculum guidance" @default.
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• W4387394711 doi "https://doi.org/10.1016/j.robot.2023.104550" @default.
• W4387394711 hasPublicationYear "2023" @default.
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