论文标题
学习一个接触自适应控制器，以进行健壮，高效的腿部运动
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion
论文作者
论文摘要
我们提出了一个层次结构框架，该框架结合了基于模型的控制和增强学习（RL），以合成四足动物（Unitree Laikago）的强大控制器。该系统由一个高级控制器组成，该控制器学会了从一组原语中进行选择，以响应环境的变化和一个使用已建立的控制方法来鲁棒地执行原始方法的低级控制器。我们的框架学习了一个可以适应即时挑战性环境变化的控制器，包括在训练过程中看不到的新型场景。与基线方法相比，学到的控制器的能源效率高达85％，并且更健壮。我们还没有任何随机化或适应方案将控制器部署在物理机器人上。
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.
