Research on multi-terrain locomotion control of quadruped robots based on an improved PPO algorithm

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The proposed LSTM-PPO algorithm shows significant improvements in adaptability, achieving higher cumulative rewards during training.
A distributed reinforcement learning framework with a multi-objective reward mechanism supports better performance on unstructured terrains.
Training was conducted using a simulation environment built on Isaac Gym, validating the algorithm's effectiveness through ablation studies.
Experimental results demonstrate the method's robust applicability in real quadruped robots navigating complex terrains like grass, slopes, and stairs.

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The proposed method enhances quadruped locomotion control, improving adaptability and stability across diverse terrains using LSTM-PPO.

Zhang et al. (2025) studied this question.

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