面向狭窄管道巡检的四足机器人运动学习
Learning Quadrupedal Robot Locomotion for Narrow Pipe Inspection
AI总结：
本文提出一种基于强化学习的方法，通过引入特权视觉信息和新的奖励函数，训练四足机器人自适应穿越狭窄管道，并在仿真和真实场景中验证了该方法即使面对意外障碍物也能有效完成任务。
摘要：
各种管道在工业场景和日常生活中被广泛使用，但管道巡检，尤其是狭窄尺寸的管道，仍然极具挑战性，耗费大量时间与制造成本。受巡检犬启发的四足机器人可以替代传统解决方案，但始终面临导航和运动困难。在本文中，我们引入一种基于强化学习（RL）的方法，训练策略使四足机器人能够自适应地穿越狭窄管道。为解决这些问题，定义了新的特权视觉信息和新的奖励函数。在仿真和真实世界场景中均完成了实验，证明所提方法即使管道内存在意外障碍物也能完成穿越管道任务。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
