ControlTac: Scaling Tactile Data with Physically Controlled Tactile Image Generation

Abstract:Vision-based tactile sensing is widely used in perception, reconstruction, and robotic manipulation, yet collecting large-scale tactile data remains costly due to diverse sensor-object interactions and inconsistencies across sensor instances. Existing approaches to scaling tactile data---simulation and free-form tactile generation---often yield unrealistically rendered signals with poor transfer to highly dynamic real-world tasks. We propose \name, a two-stage controllable tactile image generation framework that generates realistic tactile images conditioned on a single reference tactile image, contact force, and contact pose. By grounding generation in these important physical priors, \name synthesizes realistic samples across different sensors while effectively capturing task-relevant variations. Across a series of downstream tasks and real-world experiments, such as object insertion, imitation learning, and object weighting, the augmented datasets using our approach consistently improve performance and demonstrate practical utility in dynamic real-world settings. Project page: this https URL.

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO)

arXiv:2505.20498 [cs.CV]

(or arXiv:2505.20498v3 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2505.20498 Focus to learn more arXiv-issued DOI via DataCite

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