Tactile MNIST: Benchmarking Active Tactile Perception

Establishing a universal benchmark for tactile representation learning in robotic manipulation remains challenging due to the diversity of tactile sensor designs, data formats, and robot embodiments. Rather than seeking to establish such, we explore a scalable and promising direction for future development: egocentric vision paired with full-hand tactile data. To this end, we introduce HT-Bench, a large-scale multi-task benchmark for dexterous full-hand tactile sensing, comprising 10M RGB frames and 7.8M tactile frames collected across 226 tasks. HT-Bench evaluates tactile representations from three key perspectives: whether they encode meaningful contact geometry, whether they can align tactile observations with visual information, and whether they generalize to unseen tasks. To assess these capabilities, HT-Bench includes four tasks: fine-grained tactile similarity retrieval, masked tactile inpainting, vision-to-tactile synthesis, and multimodal tactile frame prediction. We further propose HandTouch, a vector-quantized vision--tactile encoder that learns tactile representations through progressive spatial, cross-modal, and temporal training. Across HT-Bench, HandTouch consistently outperforms representative tactile encoder baselines, improving Recall@5 on fine-grained tactile similarity retrieval from 74.65% to 85.23%, reducing RMSE on masked tactile inpainting from 0.022 to 0.010, and increasing OOD cIoU on vision-to-tactile synthesis from 0.628 to 0.705. These results demonstrate the effectiveness of HandTouch and suggest that large-scale egocentric full-hand tactile data provides a scalable basis for evaluating and advancing tactile representation learning in dexterous manipulation.

Tactile sensing provides robots with direct observations of contact physics, local geometry, and interaction dynamics, making it a vital source of feedback for contact-rich manipulation. However, existing tactile manipulation studies are often developed and evaluated within specific task settings, making it difficult to systematically compare how tactile information contributes under different interaction conditions. In this work, we develop a benchmark in simulation comprising 128 tasks organized around three complementary evaluation regimes: Precise, Recognition, and Dynamic. The benchmark further includes controlled static and dynamic task variants and 12 perturbation settings for evaluating robustness under changes in visual and physical conditions. These settings motivate studying tactile information as a temporally evolving interaction representation rather than only as an instantaneous observation. We therefore propose an action-conditioned latent predictive world model that integrates tactile history and learns a tactile state through prediction of future tactile latents. Building on this representation, we design a model that conditions action generation on the learned tactile state and uses predicted future tactile information to refine candidate actions through predictive tactile reflection. Experiments on the proposed benchmark show consistent gains from incorporating tactile information across the evaluated regimes. Project Page: https://tacmind-bench.github.io/

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