用强化学习让磁吸附四足机器人在不稳环境中稳定爬墙
Reinforcement Learning-based Robust Wall Climbing Locomotion Controller in Ferromagnetic Environment
- 分三阶段训练：从平地爬行到垂直爬壁，逐步引入磁吸附不确定性
- 在磁吸附时断时续条件下仍能保持90%以上成功爬行率，快速恢复脱落
- 适合做复杂金属环境下的磁吸附机器人控制，可直接用于硬件验证
我们提出一种面向四足磁吸附爬墙机器人的强化学习框架，显式处理磁脚吸附中的不确定性。通过物理建模捕捉部分接触、气隙敏感性及概率性附着失败。设计三阶段课程：(1) 在无吸附条件下学习平地爬行；(2) 逐步将重力方向转为垂直并激活吸附模型；(3) 注入随机吸附失效以增强滑脱恢复能力。所学策略在仿真中实现高成功率、强吸附保持和快速脱离恢复。相比假设完美吸附的模型预测控制（MPC）基线，本控制器在吸附间歇性丢失时仍能持续运动。在无需外接电源的硬件机器人上进行实验，验证了其在钢质表面稳定垂直爬行的能力，即使存在短暂对齐偏差和附着不全也保持稳定。结果表明，结合课程学习与真实吸附建模，可构建鲁棒的仿真到现实迁移框架，适用于复杂环境中的磁吸附爬行机器人。
原文摘要 · Abstract (English)
We present a reinforcement learning framework for quadrupedal wall-climbing locomotion that explicitly addresses uncertainty in magnetic foot adhesion. A physics-based adhesion model of a quadrupedal magnetic climbing robot is incorporated into simulation to capture partial contact, air-gap sensitivity, and probabilistic attachment failures. To stabilize learning and enable reliable transfer, we design a three-phase curriculum: (1) acquire a crawl gait on flat ground without adhesion, (2) gradually rotate the gravity vector to vertical while activating the adhesion model, and (3) inject stochastic adhesion failures to encourage slip recovery. The learned policy achieves a high success rate, strong adhesion retention, and rapid recovery from detachment in simulation under degraded adhesion. Compared with a model predictive control (MPC) baseline that assumes perfect adhesion, our controller maintains locomotion when attachment is intermittently lost. Hardware experiments with the untethered robot further confirm robust vertical crawling on steel surfaces, maintaining stability despite transient misalignment and incomplete attachment. These results show that combining curriculum learning with realistic adhesion modeling provides a resilient sim-to-real framework for magnetic climbing robots in complex environments.
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