ControlTac: Scaling Tactile Data with Physically Controlled Tactile Image Generation

arXiv:2505.20498 · cs.CV, cs.LG, cs.RO · Submitted 2025-05-26 · Read on arXiv

cs.CV, cs.LG, cs.RO

Submitted: 2025-05-26

Updated: 2026-09-08

Comments: Accepted by CoRL 2026

Project page: https://dongyuluo.github.io/controltac

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Data Augmentation — 20× in this paper · explained in 7 episodes
• Diffusion Model — 18× in this paper · explained in 8 episodes
• Downstream Tasks — 14× in this paper · explained in 2 episodes
• Transformer — 10× in this paper · explained in 2 episodes
• Diffusion Models — 7× in this paper · explained in 6 episodes
• Latent Representation — 6× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 5× in this paper
• Generative Model — 5× in this paper · explained in 2 episodes
• Generative Models — 5× in this paper · explained in 2 episodes
• Mean Squared Error — 4× in this paper
• IEEE International Conference on Robotics and Automation — 3× in this paper
• Representation Learning — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Mean Absolute Error — 2× in this paper
• Physical Constraints — 2× in this paper · explained in 7 episodes
• Structural Similarity Index Measure — 2× in this paper
• 3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• Tactile-Augmented Radiance Fields
• 9DTact: A Compact Vision-Based Tactile Sensor for Accurate 3D Shape Reconstruction and Generalizable 6D Force Estimation
• Conditional Generative Adversarial Nets
• Progressive Growing of GANs for Improved Quality, Stability, and Variation
• Understanding the Limitations of Conditional Generative Models
• Denoising Diffusion Implicit Models
• Score-Based Generative Modeling through Stochastic Differential Equations
• PIXART-{\delta}: Fast and Controllable Image Generation with Latent Consistency Models
• SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers
• DiffFluid: Plain Diffusion Models are Effective Predictors of Flow Dynamics
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• DINOv2: Learning Robust Visual Features without Supervision
• Decoupled Weight Decay Regularization

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