Robust Recovery Motion Control for Quadrupedal Robots via Learned Terrain Imagination

Understand

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.