Walking on Ice: Adaptive Gait Control of Humanoid Robots Based on Visual Prediction and Proprioceptive Estimation for Variable Friction Environments

Abstract

Humanoid robots have been increasingly deployed in real-world environments such as intelligent manufacturing, healthcare, and agriculture, from indoor spaces to outdoor terrains where friction conditions vary significantly. However, existing approaches such as domain randomization or employing estimation struggle to handle low-friction gait control and fail to address variable friction conditions, making it difficult to ensure control robustness. To address these challenges, we propose Walking on Ice (WoI), a friction-aware locomotion framework that combines proactive prediction with reactive adaptation for varying surface friction. We introduce a VLM-based friction predictor that enables the policy to take anticipatory actions before friction changing, thereby addressing variable-friction scenarios. Additionally, we design a friction estimator combined with a joint history encoder that perceives proprioceptive states to handle low-friction surfaces. Experimental results demonstrate that WoI improves success rate by 8.5% and reduces slip rate by 5.15% over the state-of-the-art method Denoising World Model Learning (DWL). Particularly, in extreme cases, WoI achieves 98.3% success rate with 51.0% higher survival rate, 3.20% lower slip rate, and 46.5% lower stuck rate than DWL, demonstrating stable and efficient gait adaptation. Our work provides a robust and efficient gait control framework for stable locomotion in real-world scenarios with dynamic friction conditions.