ZSL-RPPO：利用循环近端策略优化在挑战性地形上实现四足运动的零样本学习
ZSL-RPPO: Zero-Shot Learning for Quadrupedal Locomotion in Challenging Terrains using Recurrent Proximal Policy Optimization
AI总结：
本文提出ZSL-RPPO架构与RPPO算法，在部分可观测环境中直接训练循环策略，并通过域随机化实现四足机器人跨地形零样本仿真到现实迁移。
摘要：
我们提出了ZSL-RPPO，这是一种改进的零样本学习架构，克服了师生神经网络的局限性，能够在挑战性地形中为四足机器人生成鲁棒、可靠且通用的运动。我们提出了一种新算法RPPO（Recurrent Proximal Policy Optimization，循环近端策略优化），该算法在部分可观测环境中直接训练循环神经网络，并通过域随机化实现更鲁棒的训练。我们的运动控制器支持仿真到现实迁移过程中针对内在和外在物理参数的广泛扰动，而无需进一步微调。这可以避免学生网络在仿真到现实迁移过程中性能显著下降，从而增强运动控制器的鲁棒性和泛化能力。我们在真实环境中将控制器部署到Unitree A1和Aliengo机器人上，外部感知由固态激光雷达或深度相机提供。我们的运动控制器在湿滑表面、草地和楼梯等各种挑战性地形上进行了测试。实验结果和比较表明，我们的方法显著优于当前最先进的方法。
英文摘要：
We present ZSL-RPPO, an improved zero-shot learning architecture that overcomes the limitations of teacher-student neural networks and enables generating robust, reliable, and versatile locomotion for quadrupedal robots in challenging terrains. We propose a new algorithm RPPO (Recurrent Proximal Policy Optimization) that directly trains recurrent neural network in partially observable environments and results in more robust training using domain randomization. Our locomotion controller supports extensive perturbation across simulation-to-reality transfer for both intrinsic and extrinsic physical parameters without further fine-tuning. This can avoid the significant decline of student's performance during simulation-to-reality transfer and therefore enhance the robustness and generalization of the locomotion controller. We deployed our controller on the Unitree A1 and Aliengo robots in real environment and exteroceptive perception is provided by either a solid-state Lidar or a depth camera. Our locomotion controller was tested in various challenging terrains like slippery surfaces, Grassy Terrain, and stairs. Our experiment results and comparison show that our approach significantly outperforms the state-of-the-art.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
