TacVerse: A Multisensor Dataset and Benchmark for Cross‐Sensor Vision‐Based Tactile Perception

Vision‐based tactile sensors (VBTSs) enable robots to infer contact geometry and force‐related cues by imaging deformation through an internal camera, yet generalization across sensor designs remains poorly understood. We present TacVerse, a multisensor dataset and benchmark for cross‐sensor vision‐based tactile perception. The dataset contains 106 800 tactile images from seven VBTSs and supports three downstream tasks: shape classification, grating classification, and force regression. Experiments are conducted under three settings: within‐sensor training, zero‐shot cross‐sensor transfer, and few‐shot adaptation. Strong within‐sensor performance across all tasks indicates that the collected tactile observations are informative for the target objectives. Direct cross‐sensor transfer, however, leads to substantial degradation. Shape classification is comparatively robust, whereas grating classification and force regression are more sensitive to sensor shift. Few‐shot adaptation for force regression consistently improves performance on unseen target sensors but does not fully close the gap to within‐sensor upper bounds. A representation study further shows that Masked Autoencoder pretraining provides the most consistent gains across tasks and sensors. TacVerse provides a controlled testbed for studying sensor shift, data‐efficient adaptation, and self‐supervised learning in tactile perception.