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 locomotion 面临的重要挑战。现有方法通常依赖视觉、激光雷达等外部传感器，通过获取环境信息来提前响应障碍物。然而，在夜间或茂密森林等场景下，外部传感器往往无法正常工作，这使得机器人不得不依靠本体感知传感器来感知环境中的各类障碍物并及时做出响应。这一任务无疑充满挑战。我们的研究发现，基于碰撞检测的方法能够增强机器人对环境障碍物的感知能力。在本工作中，我们提出了一种完全基于本体感知的端到端学习型四足机器人运动控制器。该控制器能够精确检测、定位并灵敏响应未知复杂三维环境中的碰撞事件，从而提升机器人在复杂环境中的通过能力。我们在仿真和真实世界实验中均证明，该方法能使四足机器人成功穿越多种复杂环境中的困难障碍物。