Traversing 3-D complex environments has always been a significant challenge for legged locomotion. Existing methods typically rely on external sensors such as vision and lidar to preemptively react to obstacles by acquiring environmental information. However, in scenarios like nighttime or dense forests, external sensors often fail to function properly, necessitating robots to rely on proprioceptive sensors to perceive diverse obstacles in the environment and respond promptly. This task is undeniably challenging. Our research finds that methods based on collision detection can enhance a robot's perception of environmental obstacles. In this work, we propose an end-to-end learning-based quadruped robot motion controller that relies solely on proprioceptive sensing. This controller can accurately detect, localize, and agilely respond to collisions in unknown and complex 3D environments, thereby improving the robot's traversability in complex environments. We demonstrate in both simulation and real-world experiments that our method enables quadruped robots to successfully traverse challenging obstacles in various complex environments.

翻译：穿越三维复杂环境一直是足式运动面临的一项重大挑战。现有方法通常依赖视觉和激光雷达等外部传感器，通过获取环境信息来预先对障碍物做出反应。然而，在夜间或茂密森林等场景中，外部传感器常常无法正常工作，这要求机器人必须依赖本体感知传感器来感知环境中的各类障碍物并及时响应。这项任务无疑极具挑战性。我们的研究发现，基于碰撞检测的方法可以增强机器人对环境障碍物的感知能力。在本工作中，我们提出了一种仅依赖本体感知的端到端学习型四足机器人运动控制器。该控制器能够在未知且复杂的三维环境中准确检测、定位并灵巧地响应碰撞，从而提升机器人在复杂环境中的穿越能力。我们在仿真和实物实验中均证明，该方法能使四足机器人在多种复杂环境中成功穿越具有挑战性的障碍物。