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Computer Science > Robotics

arXiv:2605.00321 (cs)
[Submitted on 1 May 2026 (v1), last revised 10 Jun 2026 (this version, v2)]

Title:Embodied Interpretability: Linking Causal Understanding to Generalization in Vision-Language-Action Models

Authors:Hanxin Zhang, Mingshuo Xu, Abdulqader Dhafer, Shigang Yue, Hongbiao Dong, Zhou Daniel Hao
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Abstract:Vision-Language-Action (VLA) policies often fail under distribution shift, suggesting that decisions may depend on spurious visual correlations rather than task-relevant causes. We formulate visual-action attribution as an interventional estimation problem. Accordingly, we introduce the Interventional Significance Score (ISS), an interventional masking procedure for estimating the causal influence of visual regions on action predictions, and the Nuisance Mass Ratio (NMR), a scalar measure of attribution to task-irrelevant features. We analyze the statistical properties of ISS and show that it admits unbiased estimation, and we characterize conditions under which action prediction error provides a valid proxy for causal influence. Experiments across diverse manipulation tasks indicate that NMR predicts generalization behavior and that ISS yields more faithful explanations than existing interpretability methods. These results suggest that interventional attribution provides a simple diagnostic approach for identifying causal misalignment in embodied policies.
Comments: Accepted at the 43rd International Conference on Machine Learning (ICML 2026)
Subjects: Robotics (cs.RO)
Cite as: arXiv:2605.00321 [cs.RO]
  (or arXiv:2605.00321v2 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2605.00321
arXiv-issued DOI via DataCite

Submission history

From: Hanxin Zhang [view email]
[v1] Fri, 1 May 2026 01:00:00 UTC (12,656 KB)
[v2] Wed, 10 Jun 2026 16:22:35 UTC (16,257 KB)
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