Reinforcement Learning-based Robust Wall Climbing Locomotion Controller in Ferromagnetic Environment

TL;DR

This paper introduces a reinforcement learning-based control framework for quadrupedal magnetic wall-climbing robots that effectively handles adhesion uncertainties, demonstrating high success and robustness in simulation and real-world tests.

Contribution

It develops a curriculum learning approach with realistic adhesion modeling to improve the robustness and transferability of reinforcement learning controllers for magnetic wall climbing.

Findings

High success rate in simulation and hardware tests.

Robustness to adhesion failures and transient misalignments.

Outperforms MPC baseline under adhesion loss.

Abstract

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.