点字を読む学習：拡散モデルで触覚のリアリティギャップを埋める
Learning to Read Braille: Bridging the Tactile Reality Gap with Diffusion Models
拡散モデルを用いてシミュレーションの接触深度からリアルな触覚画像を生成し、実機の点字読み取りタスクで分類精度75.74%を達成した研究。
著者: Carolina Higuera, Byron Boots, Mustafa Mukadam
分類: cs.RO
原文アブストラクト
Simulating vision-based tactile sensors enables learning models for contact-rich tasks when collecting real world data at scale can be prohibitive. However, modeling the optical response of the gel deformation as well as incorporating the dynamics of the contact makes sim2real challenging. Prior works have explored data augmentation, fine-tuning, or learning generative models to reduce the sim2real gap. In this work, we present the first method to leverage probabilistic diffusion models for capturing complex illumination changes from gel deformations. Our tactile diffusion model is able to generate realistic tactile images from simulated contact depth bridging the reality gap for vision-based tactile sensing. On real braille reading task with a DIGIT sensor, a classifier trained with our diffusion model achieves 75.74% accuracy outperforming classifiers trained with simulation and other approaches. Project page: https://github.com/carolinahiguera/Tactile-Diffusion
