  Quadrupedal robots resemble the physical ability of legged animals to walk
through unstructured terrains. However, designing a controller for quadrupedal
robots poses a significant challenge due to their functional complexity and
requires adaptation to various terrains. Recently, deep reinforcement learning,
inspired by how legged animals learn to walk from their experiences, has been
utilized to synthesize natural quadrupedal locomotion. However,
state-of-the-art methods strongly depend on a complex and reliable sensing
framework. Furthermore, prior works that rely only on proprioception have shown
a limited demonstration for overcoming challenging terrains, especially for a
long distance. This work proposes a novel quadrupedal locomotion learning
framework that allows quadrupedal robots to walk through challenging terrains,
even with limited sensing modalities. The proposed framework was validated in
real-world outdoor environments with varying conditions within a single run for
a long distance.
