見える触覚：視覚運動ポリシーのための接触情報レンダリング
Visible Touch: Rendering Contact for Visuomotor Policies
接触信号をシーンと同じ画像空間に描画することで、アーキテクチャ変更なしに視覚ベースのポリシーへ触覚情報を統合し、低コスト磁気センサとともに操作性能を向上させた研究。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
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著者: Metin Alp Dogan, Edward Sun, Feng Xu, Daniel Wu, Allen Peng, Dennis Hong, Yuchen Cui
分類: cs.RO
原文アブストラクト
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.