CVLN-Think: Causal Inference with Counterfactual Style Adaptation for Continuous Vision-and-Language Navigation

Abstract

Vision-and-Language Navigation in Continuous Environments (VLN-CE) presents challenges due to environmental variations and domain shifts, making it difficult for agents to generalize beyond seen environments. Most existing methods rely on learning correlations between observations and actions from training data, which leads to spurious dependencies on environmental biases. To address this, we propose CVLN-Think (CVT), a novel navigation model that incorporates causal inference to enhance robustness and adaptability. Specifically, Style Causal Adjuster (SCA) generates counterfactual style observations, enabling agents to learn invariant spatial structures rather than overfitting to dataset-specific visual patterns. Furthermore, Thinking Cause Navigation Engine (TCNE) applies causal intervention to adjust navigation decisions by identifying and mitigating biases from prior experience. Unlike conventional approaches that passively learn from data distributions, our model actively thinks along the "observation-action" chain to make more reliable navigation predictions. Experimental results demonstrate that our approach achieves satisfactory performance on VLN-CE tasks. Further analysis indicates that our method possesses stronger generalization capabilities, highlighting the superiority of our proposed approach.