Quadrupedal robots have emerged as a cutting-edge platform for assisting humans, finding applications in tasks related to inspection and exploration in remote areas. Nevertheless, their floating base structure renders them susceptible to fall in cluttered environments, where manual recovery by a human operator may not always be feasible. Several recent studies have presented recovery controllers employing deep reinforcement learning algorithms. However, these controllers are not specifically designed to operate effectively in cluttered environments, such as stairs and slopes, which restricts their applicability. In this study, we propose a robust all-terrain recovery policy to facilitate rapid and secure recovery in cluttered environments. We substantiate the superiority of our proposed approach through simulations and real-world tests encompassing various terrain types.

翻译：四足机器人作为辅助人类的尖端平台，已应用于远程区域的巡检与勘探等任务。然而，其浮动基座结构使其在杂乱环境中容易摔倒，而人工操作员的手动恢复并非总是可行。近年来，多项研究提出了采用深度强化学习算法的恢复控制器。但这些控制器并非专门针对楼梯、坡道等杂乱环境设计，限制了其应用范围。在本研究中，我们提出一种鲁棒的全地形恢复策略，以实现在杂乱环境中的快速安全恢复。通过涵盖多种地形类型的仿真与真实环境测试，我们验证了所提方法的优越性。