A Bio-Inspired Compliance Planning and Implementation Method for Hydraulically Actuated Quadruped Robots with Consideration of Ground Stiffness

Zhang, Xiaoxing; Yi, Haoyuan; Liu, Junjun; Li, Qi; Luo, Xin

doi:10.3390/s21082838

Cited by 3 publications

(1 citation statement)

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“…The ultimate goal of biorobotics engineers and scientists is to draw inspiration from biological organisms in nature and to achieve lifelike biomimetic motion by mimicking their behaviors, ultimately contributing to human production and development [ 1 , 2 ]. Quadruped robots are designed to imitate quadrupedal creatures such as dogs and horses [ 3 , 4 ]. With early researchers exploring their mechanical structures, existing quadruped robots share similar forms and designs.…”

Section: Introductionmentioning

confidence: 99%

Learning and Reusing Quadruped Robot Movement Skills from Biological Dogs for Higher-Level Tasks

Wan,

Luo,

Meng

et al. 2023

Sensors

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In the field of quadruped robots, the most classic motion control algorithm is based on model prediction control (MPC). However, this method poses challenges as it necessitates the precise construction of the robot’s dynamics model, making it difficult to achieve agile movements similar to those of a biological dog. Due to these limitations, researchers are increasingly turning to model-free learning methods, which significantly reduce the difficulty of modeling and engineering debugging and simultaneously reduce real-time optimization computational burden. Inspired by the growth process of humans and animals, from learning to walk to fluent movements, this article proposes a hierarchical reinforcement learning framework for the motion controller to learn some higher-level tasks. First, some basic motion skills can be learned from motion data captured from a biological dog. Then, with these learned basic motion skills as a foundation, the quadruped robot can focus on learning higher-level tasks without starting from low-level kinematics, which saves redundant training time. By utilizing domain randomization techniques during the training process, the trained policy function can be directly transferred to a physical robot without modification, and the resulting controller can perform more biomimetic movements. By implementing the method proposed in this article, the agility and adaptability of the quadruped robot can be maximally utilized to achieve efficient operations in complex terrains.

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