We present a hierarchical framework that combines model-based control and reinforcement learning (RL) to synthesize robust controllers for a quadruped (the Unitree Laikago). The system consists of a high-level controller that learns to choose from a set of primitives in response to changes in the environment and a low-level controller that utilizes an established control method to robustly execute the primitives. Our framework learns a controller that can adapt to challenging environmental changes on the fly, including novel scenarios not seen during training. The learned controller is up to 85~percent more energy efficient and is more robust compared to baseline methods. We also deploy the controller on a physical robot without any randomization or adaptation scheme.

中文翻译：

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学习用于稳健、高效的腿式运动的接触自适应控制器

我们提出了一个分层框架，它结合了基于模型的控制和强化学习 (RL) 来合成四足动物（Unitree Laikago）的鲁棒控制器。该系统由一个高级控制器组成，该控制器学习从一组原语中进行选择以响应环境的变化，以及一个低级控制器，该控制器利用已建立的控制方法来稳健地执行原语。我们的框架学习了一个控制器，该控制器可以动态适应具有挑战性的环境变化，包括训练期间未见过的新场景。与基线方法相比，学习控制器的能效提高了 85%，并且更加稳健。我们还在没有任何随机化或适应方案的情况下将控制器部署在物理机器人上。