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DIVA introduces a novel architectural module that dynamically regulates visual token relevance within Vision-Language-Action (VLA) policies, effectively filtering out task-irrelevant noise without external supervision. By combining high-level intent with low-level evidence in a dual-space design, the method significantly boosts success rates on the LIBERO benchmark and demonstrates improved robustness against real-world visual perturbations. This advancement addresses a critical bottleneck in current VLA models by optimizing how visual context influences action computation.
DIVA: Dual-Space Intent-Aware Visual Attenuation for Vision-Language-Action Policies
arXiv:2610.09144v1 Announce Type: new Abstract: Vision-language-action (VLA) policies typically feed dense visual patch tokens into a language-action backbone, preserving scene context but offering no explicit mechanism to regulate how strongly different visual tokens influence policy computation. We introduce DIVA, a Dual-Space Intent-Aware Visual Attenuation module with an anchor-then-attenuate design. DIVA combines high-level task intent with low-level visual evidence to estimate patch-wise relevance anchors, then applies them in two complementary spaces: it reweights projected visual tokens before backbone entry and persistently attenuates low-relevance visual states within the backbone. DIVA preserves the full visual token sequence and requires no external grounding supervision. On LIBERO, DIVA improves OpenVLA-OFT from 96.6% to 98.0% average success and raises its zero-shot LIBERO-Plus score from 69.6 to 72.6. Real-world experiments further show consistent gains under task-irrelevant visual perturbations, supporting the robustness of intent-aware visual attenuation beyond simulation.
Abstract:Vision-language-action (VLA) policies typically feed dense visual patch tokens into a language-action backbone, preserving scene context but offering no explicit mechanism to regulate how strongly different visual tokens influence policy computation. We introduce DIVA, a Dual-Space Intent-Aware Visual Attenuation module with an anchor-then-attenuate design. DIVA combines high-level task intent with low-level visual evidence to estimate patch-wise relevance anchors, then applies them in two complementary spaces: it reweights projected visual tokens before backbone entry and persistently attenuates low-relevance visual states within the backbone. DIVA preserves the full visual token sequence and requires no external grounding supervision. On LIBERO, DIVA improves OpenVLA-OFT from 96.6% to 98.0% average success and raises its zero-shot LIBERO-Plus score from 69.6 to 72.6. Real-world experiments further show consistent gains under task-irrelevant visual perturbations, supporting the robustness of intent-aware visual attenuation beyond simulation.
Artificial Intelligence (cs.AI)
arXiv:2610.09144 [cs.AI]
(or arXiv:2610.09144v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.09144 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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