Deep Learning Frameworks for Robust Quadrupedal Locomotion
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These sources detail advanced **reinforcement learning frameworks** designed to improve how **quadruped robots** navigate difficult, real-world environments. The first source introduces a **single-stage teacher-student method** that utilizes **skeleton information** and a system-response model to achieve more natural, stable movement. The second source proposes **ZSL-RPPO**, a zero-shot learning architecture that eliminates the need for imitation by training **recurrent neural networks** directly in partially observable settings. Both research papers prioritize bridging the **simulation-to-reality gap**, ensuring robots can handle unpredictable terrain like stairs, oily surfaces, and grass. By employing **domain randomization** and specialized encoders, these frameworks enhance the **robustness and adaptability** of robotic locomotion without requiring extensive manual tuning. Together, they represent a shift toward more **efficient training paradigms** that produce versatile and resilient autonomous behaviors.
Sources:
1)
October 22 2025
Skeleton Information-Driven Reinforcement Learning Framework for Robust and Natural Motion of Quadruped Robots
Guangdong University of Technology, University of Macau
Huiyang Cao, Hongfa Lei, Yangjun Liu, Zheng Chen, Shuai Shi, Bingquan Li, Weichao Xu, Zhi-Xin Yang
https://doi.org/10.3390/sym17111787
2)
March 2024
ZSL-RPPO: Zero-Shot Learning for Quadrupedal Locomotion in Challenging Terrains using Recurrent Proximal Policy Optimization
Huawei Technologies, Huawei Munich Research Center, University College London, Huawei Noah's Ark Lab, East China Normal University
Yao Zhao, Tao Wu, Yijie Zhu, Xiang Lu, Jun Wang, Haitham Bou-Ammar, Xinyu Zhang, Peng Du
https://arxiv.org/pdf/2403.01928
3)
May 2025
End-to-End Multi-Task Policy Learning from NMPC for Quadruped Locomotion
Bonn-Rhein-Sieg University of Applied Sciences, University of Bonn, Fraunhofer Institute for Intelligent Analysis and Information Systems
Anudeep Sajja, Shahram Khorshidi, Sebastian Houben, Maren Bennewitz
https://arxiv.org/pdf/2505.08574
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