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<p>Hydraulic legged robots have potential for high-dynamic motion due to their large power-to-weight ratios. However, it is challenging to ensure both stability and continuity in the motion of such robots. In this study, we propose a jumping motion control framework based on deep reinforcement learning that enables hydraulic limb leg units to perform stable and continuous jumping motions. First, to accurately represent the performance of a physical prototype, a quasi-realistic model incorporating physical feasibility constraints is constructed. This model is informed by analysis of the relevant fluid dynamics, and incorporates a trajectory generator and a motion tracking controller. To achieve stable and continuous jumping performance, a deep reinforcement learning algorithm is developed, which jointly optimizes the trajectory generator and the motion tracking controller. Through validation on the physical prototype, we demonstrate that the proposed method reduces the maximum deviation and the average deviation by over 47% and 60%, respectively, and improves landing compliance by up to 7.7% compared to a baseline optimization algorithm, the non-dominated sorting genetic algorithm (NSGA-II). The proposed control framework may serve as a reference for high-dynamic motion control of legged robots and multi-objective optimization across several decision variables.</p>

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Stable and continuous vertical jumping control of hydraulic legged robots through reinforcement learning

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • Junhui Zhang,
  • Pengyuan Ji,
  • Lizhou Fang,
  • Jinyuan Liu,
  • Dandan Wang,
  • Jikun Ai,
  • Huaizhi Zong,
  • Bing Xu

摘要

Hydraulic legged robots have potential for high-dynamic motion due to their large power-to-weight ratios. However, it is challenging to ensure both stability and continuity in the motion of such robots. In this study, we propose a jumping motion control framework based on deep reinforcement learning that enables hydraulic limb leg units to perform stable and continuous jumping motions. First, to accurately represent the performance of a physical prototype, a quasi-realistic model incorporating physical feasibility constraints is constructed. This model is informed by analysis of the relevant fluid dynamics, and incorporates a trajectory generator and a motion tracking controller. To achieve stable and continuous jumping performance, a deep reinforcement learning algorithm is developed, which jointly optimizes the trajectory generator and the motion tracking controller. Through validation on the physical prototype, we demonstrate that the proposed method reduces the maximum deviation and the average deviation by over 47% and 60%, respectively, and improves landing compliance by up to 7.7% compared to a baseline optimization algorithm, the non-dominated sorting genetic algorithm (NSGA-II). The proposed control framework may serve as a reference for high-dynamic motion control of legged robots and multi-objective optimization across several decision variables.