用强化学习让四足机器人自适应穿越狭窄管道。
Learning Quadrupedal Robot Locomotion for Narrow Pipe Inspection
- 基于强化学习训练四足机器人穿越狭窄管道的策略。
- 在仿真与真实场景中均成功完成任务，可应对内部意外障碍。
- 适合需要灵活巡检的工业场景，如狭小管道检修。
各类管道在工业和日常生活中广泛应用，但狭窄管道的检测仍极具挑战，耗时且成本高。受巡检犬启发的四足机器人可替代传统方案，但面临导航与运动控制难题。本文提出一种基于强化学习的方法，训练机器人策略以自适应穿越狭窄管道。设计了新的特权视觉信息与奖励函数，有效解决环境感知与运动规划问题。在仿真与真实场景中均完成测试，结果表明该方法可在存在意外障碍的情况下成功完成管道穿越任务。
原文摘要 · Abstract (English)
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.
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