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.

翻译：足式机器人凭借其谨慎选择落足点和灵活调整身体姿态的能力，在穿越复杂地形和进入传统平台无法触及的受限空间方面展现出巨大潜力。然而，在实际应用中的鲁棒部署仍是一大挑战。本文提出一种基于强化学习和三维体积表示的足式运动控制方法，使机器人在受限和无结构环境中实现稳健且多功能的运动。通过采用双层分层策略架构，我们既利用了高度鲁棒的低层策略来执行六维指令，也借助高层策略实现三维空间感知能力以在悬空障碍物下导航。研究包含开发程序化地形生成器以创建多样化训练环境，并通过仿真与实物环境中的系列实验评估，证明了该方法在控制四足机器人穿越受限崎岖地形时的有效性。本工作将足式机器人的应用范围拓展至更广阔的场景。