  Legged robots have the potential to traverse complex terrain and access
confined spaces beyond the reach of traditional platforms thanks to their
ability to carefully select footholds and flexibly adapt their body posture
while walking. However, robust deployment in real-world applications is still
an open challenge. In this paper, we present a method for legged locomotion
control using reinforcement learning and 3D volumetric representations to
enable robust and versatile locomotion in confined and unstructured
environments. By employing a two-layer hierarchical policy structure, we
exploit the capabilities of a highly robust low-level policy to follow 6D
commands and a high-level policy to enable three-dimensional spatial awareness
for navigating under overhanging obstacles. Our study includes the development
of a procedural terrain generator to create diverse training environments. We
present a series of experimental evaluations in both simulation and real-world
settings, demonstrating the effectiveness of our approach in controlling a
quadruped robot in confined, rough terrain. By achieving this, our work extends
the applicability of legged robots to a broader range of scenarios.
