Visible Touch: Rendering Contact for Visuomotor Policies

• URL: http://arxiv.org/abs/2609.14156v1
• Date: Sat, 12 Sep 2026 21:15:56 GMT
• Title: Visible Touch: Rendering Contact for Visuomotor Policies
• Abstract summary: Contact information is essential to robust manipulation of visuomotor policies.<n>Current approaches require specialized tactile hardware, add separate tactile encoders, or commit to non-image policy backbones.<n>We operationalize this insight in Visible Touch, paired with a custom low-cost magnetic contact sensor.
• Score: 5.457564982234203
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Integrating contact information into visuomotor policies remains an open problem. Touch is essential to robust manipulation, yet most modern policies, including pretrained vision-language-action (VLA) models, operate from vision and proprioception alone. Existing approaches to closing this gap require specialized tactile hardware, add separate tactile encoders, or commit to non-image policy backbones, all incompatible with the modern paradigm of image-conditioned policies built on pretrained 2D visual representations. Our key insight is that the bottleneck is not the contact information itself, but how it is delivered: when contact signals are exposed in the same spatial frame as the scene the policy already attends to, they become directly usable by any image-conditioned policy without architectural changes. We operationalize this insight in Visible Touch, paired with a custom low-cost magnetic contact sensor that is open-sourced and fabricated from off-the-shelf parts via a parametric CAD-to-mold pipeline. Across the LIBERO benchmark, Visible Touch improves BC-Transformer success by 15.7 percentage points on average in the 2-view setting, with similar gains in the 1-view setting; controlled comparisons show that the contact-integration strategy strongly affects how effectively tactile information is used. The pattern holds when fine-tuning pretrained VLAs: miniVLA on LIBERO gains 25 percentage points on average, and $π_{0.5}$ on four real-world contact-rich tasks gains 30 percentage points with our custom sensor.

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