Various pipes are extensively used in both industrial settings and daily life, but the pipe inspection especially those with narrow sizes are still very challenging with tremendous time and manufacturing consumed. Quadrupedal robots, inspired from patrol dogs, can be a substitution of traditional solutions but always suffer from navigation and locomotion difficulties. In this paper, we introduce a Reinforcement Learning (RL) based method to train a policy enabling the quadrupedal robots to cross narrow pipes adaptively. A new privileged visual information and a new reward function are defined to tackle the problems. Experiments on both simulation and real world scenarios were completed, demonstrated that the proposed method can achieve the pipe-crossing task even with unexpected obstacles inside.

翻译：各类管道在工业场景和日常生活中广泛应用，但管道巡检尤其是狭窄尺寸管道的检测仍然极具挑战性，需要耗费大量时间和制造成本。受巡逻犬启发而设计的四足机器人可作为传统解决方案的替代，但其在导航与运动控制方面始终存在困难。本文提出一种基于强化学习的方法，通过训练策略使四足机器人能够自适应地穿越狭窄管道。为此，我们定义了新的特权视觉信息与奖励函数以解决相关问题。在仿真环境与真实场景中完成的实验表明，即使管道内部存在意外障碍物，所提方法仍能成功完成管道穿越任务。