[2304.01182] Learning to Read Braille: Bridging the Tactile Reality Gap with Diffusion Models

arxiv.org · 6,058 words · saved by 1 readers

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: this https URL

Learning to Read Braille: Bridging the Tactile Reality Gap with Diffusion Models Carolina Higuera1 , Byron Boots1 , and Mustafa Mukadam2 1 University of Washington, 2 Meta AI arXiv:2304.01182v1 [cs.RO] 3 Apr 2023 Fig. 1: (Left) Our tactile diffusion model, trained on YCB-Slide and fine-tuned on 20% real braille, learns to generate realistic…

[2304.01182] Learning to Read Braille: Bridging the Tactile Reality Gap with Diffusion Models

related reading