CofactVLA: Deconfounding Vision-Language-Action Models via Counterfactual Intervention
Vision-Language-Action (VLA) models have driven significant progress in robotic manipulation, yet they fundamentally struggle with the vision-override phenomenon. Driven by the severe modality imbalance between dense visual streams and sparse linguistic instructions, VLAs frequently fall prey to causal confusion. Instead of treating language as the primary causal driver, the policy entirely bypasses the original instruction by overfitting to spurious visual confounders, such as prominent objects or familiar layouts. To systematically alleviate this bias, we formalize the process of action generation as a Dual-path Deconfounding Graph (DDG) and propose CofactVLA, a novel causal interventio...
| Authors | Yan Zhang, Yinan Wu, Haoran Duan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 5 August 2026 |
| Last updated at source | 5 August 2026 |
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