  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.
