论文标题
实践使完美：一种迭代的方法，可以实现腿部机器人的精确跟踪
Practice Makes Perfect: an iterative approach to achieve precise tracking for legged robots
论文作者
论文摘要
腿部机器人的精确轨迹跟踪由于其高度的自由度，未建模的非线性动态或环境的随机干扰而可能具有挑战性。克服这些挑战的通常采用的解决方案是使用基于优化的算法并使用简化的，还原的模型近似系统。此外，深层神经网络正在成为实现敏捷和强大的腿部运动的更有前途的选择。但是，这些方法要么需要大量的机载计算，要么需要从一个机器人那里收集数百万个数据点。为了解决这些问题并改善跟踪性能，本文提出了一种基于迭代学习控制的方法。这种方法使一个机器人只需在几次试验中利用腿部运动的重复性，从自己的错误中学习。然后，将扭矩库作为查找表创建，因此机器人无需重复计算或一遍又一遍地学习相同的技能。这个过程类似于动物如何学习自然界的肌肉记忆。在模拟环境中，在A1机器人上测试了所提出的方法，它允许机器人以不同的速度下pon，而精确地遵循参考轨迹而没有大量计算。
Precise trajectory tracking for legged robots can be challenging due to their high degrees of freedom, unmodeled nonlinear dynamics, or random disturbances from the environment. A commonly adopted solution to overcome these challenges is to use optimization-based algorithms and approximate the system with a simplified, reduced-order model. Additionally, deep neural networks are becoming a more promising option for achieving agile and robust legged locomotion. These approaches, however, either require large amounts of onboard calculations or the collection of millions of data points from a single robot. To address these problems and improve tracking performance, this paper proposes a method based on iterative learning control. This method lets a robot learn from its own mistakes by exploiting the repetitive nature of legged locomotion within only a few trials. Then, a torque library is created as a lookup table so that the robot does not need to repeat calculations or learn the same skill over and over again. This process resembles how animals learn their muscle memories in nature. The proposed method is tested on the A1 robot in a simulated environment, and it allows the robot to pronk at different speeds while precisely following the reference trajectories without heavy calculations.
