Achieving precise target jumping with legged robots poses a significant challenge due to the long flight phase and the uncertainties inherent in contact dynamics and hardware. Forcefully attempting these agile motions on hardware could result in severe failures and potential damage. Motivated by these challenging problems, we propose an Iterative Learning Control (ILC) approach that aims to learn and refine jumping skills from easy to difficult, instead of directly learning these challenging tasks. We verify that learning from simplicity can enhance safety and target jumping accuracy over trials. Compared to other ILC approaches for legged locomotion, our method can tackle the problem of a long flight phase where control input is not available. In addition, our approach allows the robot to apply what it learns from a simple jumping task to accomplish more challenging tasks within a few trials directly in hardware, instead of learning from scratch. We validate the method via extensive experiments in the A1 model and hardware for various jumping tasks. Starting from a small jump (e.g., a forward leap of 40cm), our learning approach empowers the robot to accomplish a variety of challenging targets, including jumping onto a 20cm high box, jumping to a greater distance of up to 60cm, as well as performing jumps while carrying an unknown payload of 2kg. Our framework can allow the robot to reach the desired position and orientation targets with approximate errors of 1cm and 1 degree within a few trials.

翻译：实现腿式机器人的精确目标跳跃是一项重大挑战，这源于其较长的飞行阶段以及接触动力学和硬件固有的不确定性。在硬件上强行尝试这些敏捷动作可能导致严重故障和潜在损坏。受这些挑战性问题的启发，我们提出了一种迭代学习控制方法，旨在从易到难地学习和优化跳跃技能，而非直接学习这些高难度任务。我们验证了从简单任务开始学习能够通过多次试验提升安全性和目标跳跃精度。与其他用于腿式运动的ILC方法相比，我们的方法能够处理控制输入不可用的长飞行阶段问题。此外，我们的方法使机器人能够将其从简单跳跃任务中学到的经验应用于硬件上，仅通过数次试验即可直接完成更具挑战性的任务，而无需从头开始学习。我们通过在A1模型和硬件上进行大量跳跃任务实验验证了该方法。从一个小跳跃开始（例如向前跳跃40厘米），我们的学习方法使机器人能够完成各种挑战性目标，包括跳上20厘米高的箱子、跳跃至更远距离（达60厘米），以及在携带2公斤未知负载的情况下执行跳跃。我们的框架能使机器人在数次试验内以约1厘米和1度的近似误差达到期望的位置和姿态目标。