# TacVerse: A Multi-Sensor Dataset and Benchmark for Cross-Sensor Vision-Based Tactile Perception

arXiv: https://arxiv.org/abs/2606.25877 · HTML: https://mlsanity.com/paper/2606.25877/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2026.

## Built on by (most cited first, 2 of 2)

- [Tactus: Open-Vocabulary Object Recognition from Low-Cost Pressure Arrays](https://mlsanity.com/paper/2608.04043/) (2026)
- [BIDETA: Brain-Inspired Data-Efficient Tactile Adaptation for Unseen Sensors](https://mlsanity.com/paper/2609.08673/) (2026)

## Abstract (arXiv)

Vision-based tactile sensors (VBTSs) enable robots to infer contact geometry and force-related cues by imaging deformation through an internal camera, yet generalisation across sensor designs remains poorly understood. We present TacVerse, a multi-sensor 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 MAE (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.