Recent advancements in optimal control and reinforcement learning have enabled quadrupedal robots to perform various agile locomotion tasks over diverse terrains. During these agile motions, ensuring the stability and resiliency of the robot is a primary concern to prevent catastrophic falls and mitigate potential damages. Previous methods primarily focus on recovery policies after the robot falls. There is no active safe falling solution to the best of our knowledge. In this paper, we proposed Guardians as You Fall (GYF), a safe falling/tumbling and recovery framework that can actively tumble and recover to stable modes to reduce damage in highly dynamic scenarios. The key idea of GYF is to adaptively traverse different stable modes via active tumbling before the robot shifts to irrecoverable poses. Via comprehensive simulation and real-world experiments, we show that GYF significantly reduces the maximum acceleration and jerk of the robot base compared to the baselines. In particular, GYF reduces the maximum acceleration and jerk by 20%~73% in different scenarios in simulation and real-world experiments. GYF offers a new perspective on safe falling and recovery in locomotion tasks, potentially enabling much more aggressive explorations of existing agile locomotion skills.

翻译：在最优控制和强化学习领域的最新进展中，四足机器人已能在多种地形上执行灵活动作任务。在这些敏捷运动过程中，确保机器人的稳定性和抗扰动能力是防止灾难性坠落并减轻潜在损伤的首要关注点。以往方法主要聚焦于机器人跌倒后的恢复策略，据我们所知，目前尚无主动安全跌倒解决方案。本文提出"坠落之际，守护者"（GYF）框架——一种面向安全跌倒/翻滚与恢复的系统，能在高度动态场景中主动翻滚并恢复至稳定模式以降低损伤。GYF的核心思想是在机器人进入不可恢复姿态前，通过主动翻滚自适应地穿越不同稳定模式。通过全面的仿真与真实世界实验，我们证明GYF相比基线方法能显著降低机器人本体的最大加速度与冲击度。具体而言，在不同仿真与真实场景中，GYF将最大加速度和冲击度降低了20%~73%。GYF为运动任务中的安全跌倒与恢复提供了新视角，有望支撑现有敏捷运动技能的更激进探索。