ZSL-RPPO introduces a novel approach to quadrupedal locomotion by leveraging RPPO to train recurrent neural networks. The method eliminates the need for a teacher-student framework and supports simulation-to-reality transfer without performance degradation. Extensive experiments demonstrate superior performance compared to existing methods across various challenging terrains like slippery surfaces, grassy terrain, and stairs.
The content delves into the challenges of traditional locomotion control algorithms and highlights the benefits of reinforcement learning approaches. It emphasizes the importance of robust training under domain randomization to achieve successful simulation-to-reality transfer. The study showcases real-world applications on Unitree A1 and Aliengo robots, validating the effectiveness of ZSL-RPPO in diverse environments.
Furthermore, detailed descriptions of system overview, observation spaces, reward shaping, and domain randomization techniques provide insights into the technical aspects of the proposed approach. The implementation on hardware platforms and experimental evaluations underscore the practicality and efficiency of ZSL-RPPO in real-world scenarios.
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