Learning an Adaptive Fall Recovery Controller for Quadrupeds on Complex Terrains

Legged robots have made significant strides in locomotion However, in extreme or complex natural environments, capabilities, demonstrating impressive performance in robots still face the inevitability of falling. A major challenge tasks such as dynamic walking, running, and even complex in current research lies in developing adaptive controllers maneuvers like backflips [8], [2]. However, the ability to for robots to effectively recover from falls, allowing them recover from falls, especially on challenging and unpredictable to resume movement or efficiently complete tasks. However, terrains, remains a critical challenge in the field of legged model-based methods are often inadequate for these dynamic robotics. While substantial progress has been made in recovery tasks. For example, Mordatch et al. [12] proposed a framework strategies for flat or moderately uneven surfaces [7], [13], that optimizes automatic recovery through contact invariance, the problem of robust recovery on highly irregular terrains - but the reliance on predefined potential contact points limits such as rocky landscapes, steep inclines, or complex gaps - the exploration of flexible behaviors. In addition, classical has received limited attention.