Seemingly in defiance of basic physics, cats consistently land on their feet after falling. In this paper, we design a controller that lands the Mini Cheetah quadruped robot on its feet as well. Specifically, we explore how trajectory optimization and machine learning can work together to enable highly dynamic bioinspired behaviors. We find that a reflex approach, in which a neural network learns entire state trajectories, outperforms a policy approach, in which a neural network learns a mapping from states to control inputs. We validate our proposed controller in both simulation and hardware experiments, and are able to land the robot on its feet from falls with initial pitch angles between -90 and 90 degrees.
翻译:似乎无视基本物理学,猫在坠落后总是在脚上落地。 在本文中,我们设计了一个控制器,将迷你切塔四重机器人也落脚。 具体地说,我们探索轨迹优化和机器学习如何能共同发挥作用,使高度动态的生物激励行为得以发生。 我们发现,神经网络在反射方法中学习了整个州轨迹,超过了政策方法,神经网络在其中学习了从各州到控制投入的绘图。 我们在模拟和硬件实验中验证了我们提议的控制器,并且能够将机器人从90到90度的最初倾角从瀑布降落在脚上。