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

arXiv: https://arxiv.org/abs/2510.20174 · HTML: https://mlsanity.com/paper/2510.20174/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 8 of 8)

- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion](https://mlsanity.com/paper/2202.05481/) (2022)
- [DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning](https://mlsanity.com/paper/2301.10602/) (2023)
- [A Learning Framework for Diverse Legged Robot Locomotion Using Barrier-Based Style Rewards](https://mlsanity.com/paper/2409.15780/) (2024)
- [Imitating and Finetuning Model Predictive Control for Robust and Symmetric Quadrupedal Locomotion](https://mlsanity.com/paper/2311.02304/) (2023)
- [Magnecko: Design and Control of a Quadrupedal Magnetic Climbing Robot](https://mlsanity.com/paper/2504.13672/) (2025)

## Abstract (arXiv)

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.