IVRA: Improving Visual-Token Relations for Robot Action Policy with Training-Free Hint-Based Guidance
Many Vision-Language-Action (VLA) models flatten image patches into a 1D token sequence, weakening the 2D spatial cues needed for precise manipulation. We introduce IVRA, a lightweight, training-free method that improves spatial understanding by exploiting affinity hints already available in the model's built-in vision encoder, without requiring any external encoder or retraining. IVRA selectively injects these affinity signals into a language-model layer in which instance-level features reside. This inference-time intervention realigns visual-token interactions and better preserves geometric structure while keeping all model parameters fixed. We demonstrate the generality of IVRA by applying it to diverse VLA architectures (LLaRA, OpenVLA, and FLOWER) across simulated benchmarks spanning both 2D and 3D manipulation (VIMA and LIBERO) and on various real-robot tasks. On 2D VIMA, IVRA improves average success by +4.2% over the baseline LLaRA in a low-data regime. On 3D LIBERO, it yields consistent gains over the OpenVLA and FLOWER baselines, including improvements when baseline accuracy is near saturation (96.3% to 97.1%). All code and models will be released publicly. Visualizations are available at: jongwoopark7978.github.io/IVRA
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Jongwoo Park
admin_assigned
2026-01-27T14:37:12.018Z
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Jongwoo Park
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Kanchana Ranasinghe
admin_assigned
2026-01-27T14:37:18.435Z
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Kanchana Ranasinghe
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Jinhyeok Jang
admin_assigned
2026-01-27T14:37:25.012Z
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Jinhyeok Jang
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Cristina Mata
admin_assigned
2026-01-27T14:37:31.685Z
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Cristina Mata
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Yoo Sung Jang
admin_assigned
2026-01-27T14:37:37.481Z
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Yoo Sung Jang
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Michael S Ryoo
IVRA: Improving Visual-Token Relations for Robot Action Policy with Training-Free Hint-Based Guidance
Many Vision-Language-Action (VLA) models flatten image patches into a 1D token sequence, weakening the 2D spatial cues needed for precise manipulation. We introduce IVRA, a lightweight, training-free method that improves spatial understanding by exploiting affinity hints already available in the model's built-in vision encoder, without requiring any external encoder or retraining. IVRA selectively injects these affinity signals into a language-model layer in which instance-level features reside. This inference-time intervention realigns visual-token interactions and better preserves geometric structure while keeping all model parameters fixed. We demonstrate the generality of IVRA by applying it to diverse VLA architectures (LLaRA, OpenVLA, and FLOWER) across simulated benchmarks spanning both 2D and 3D manipulation (VIMA and LIBERO) and on various real-robot tasks. On 2D VIMA, IVRA improves average success by +4.2% over the baseline LLaRA in a low-data regime. On 3D LIBERO, it yields consistent gains over the OpenVLA and FLOWER baselines, including improvements when baseline accuracy is near saturation (96.3% to 97.1%). All code and models will be released publicly. Visualizations are available at: jongwoopark7978.github.io/IVRA
IVRA enhances spatial understanding in vision-language-action models by injecting affinity signals into language-model layers without retraining or external encoders.
Vision-Language-Action models vision encoder language-model layer visual-token interactions geometric structure inference-time intervention affinity hints instance-level features