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Sensing Which Modality Matters: Evidence-Gated Regularization for Robust VLA Policies

Summary

Vision-Language-Action (VLA) policies struggle when trained on limited, homogeneous robot demonstrations: they pick up spurious cross-sensor correlations instead of task-relevant signal, a failure the authors call modality entanglement. This makes them brittle to occlusions, sensor corruption, and distractors. The paper introduces Evidence-Gated Regularization (EGR), a training objective with zero inference-time overhead. EGR uses per-frame, per-sensor evidence estimates to enforce invariance on low-evidence sensors and single-sensor sufficiency on high-evidence ones. On a BEHAVIOR-1K-based benchmark and two real-robot setups, EGR substantially improves success rates, including +120% under single-sensor fallback and +183% on a bimanual platform with physical distractors.

SourcearXiv RoboticsAuthor: Yue Yang, Diego Romeres, Chiori Hori, Gedas Bertasius, Daniel Szafir, Siddarth Jain
Sensing Which Modality Matters: Evidence-Gated Regularization for Robust VLA Policies
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[Submitted on 2 Sep 2026]

Title:Sensing Which Modality Matters: Evidence-Gated Regularization for Robust VLA Policies

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Abstract:Vision-Language-Action (VLA) policies fuse multimodal sensory inputs, but training on limited and homogeneous robot demonstrations encourages spurious inter-sensor correlations rather than task-relevant signal, a failure we term modality entanglement. Under real-world occlusions and distractors, this manifests as nuisance sensitivity to corruption of uninformative sensors and single-modality insufficiency when only one informative sensor remains intact. We propose Evidence-Gated Regularization (EGR), a modality-agnostic training objective that introduces zero inference-time overhead. EGR derives a per-frame and per-sensor task-relevance signal to gate two state-conditional consistency objectives: invariance on low-evidence sensors, and single-sensor sufficiency on high-evidence ones. We introduce a benchmark based on BEHAVIOR-1K, comprising a fast inference-only diagnostic suite and 47 rollout-based skills targeting modality entanglement. We validate EGR on this benchmark and on two real-robot setups with fundamentally different embodiments: a bi-manual setup with two Kinova arms and three RGB cameras, and a single-arm MELFA ASSISTA setup combining vision and GelSight tactile sensors. EGR improves simulation success rates (SR) from 12.5% to 16.4% under full modalities (+31%), from 9.4% to 16.5% under uninformative-sensor corruption (+75%), and from 2.8% to 6.1% under single-sensor fallback (+120%). Under physical-object distractors, EGR boosts SR from 30% to 85% on the bi-manual setup (+183%) and from 55% to 70% on the tactile setup (+27%).

Subjects:

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

Cite as: arXiv:2609.03142 [cs.RO]

(or arXiv:2609.03142v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.03142

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yue Yang [view email] [v1] Wed, 2 Sep 2026 20:25:21 UTC (4,384 KB)

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Key points and analysis

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Key points

  • VLA policies trained on limited, homogeneous data learn spurious cross-sensor correlations, a failure mode called modality entanglement.
  • Evidence-Gated Regularization (EGR) gates two state-conditional consistency objectives using per-frame/per-sensor evidence, adding zero inference-time overhead.
  • In simulation, EGR raises success rates from 12.5% to 16.4% under full modalities, from 9.4% to 16.5% under uninformative-sensor corruption, and from 2.8% to 6.1% under single-sensor fallback.
  • On real robots with physical distractors, EGR boosts success rates from 30% to 85% on the bimanual Kinova setup and from 55% to 70% on the MELFA ASSISTA tactile setup.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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