RoboTactile: Benchmarking the Robustness of Visuo-Tactile Manipulation under Tactile Observation Failures

Tactile perception plays a vital role in precise robotic manipulation. Despite their strong performance, existing vision-tactile models may suffer from tactile observation failures caused by contact-induced wear, sensor noise, and signal delays, raising concerns about the reliability of robotic systems. However, existing benchmarks primarily focus on performance under ideal tactile conditions, leaving robustness to tactile failures insufficiently explored. To systematically evaluate this robustness, we introduce RoboTactile, a benchmark built on UniVTAC and its Isaac Sim environment. Grounded in sensing and deployment failure mechanisms, we design 14 controlled tactile observation faults spanning four dimensions: Observation Availability, Signal Fidelity, Temporal Integrity, and Contextual Consistency. The benchmark records contact-phase annotations, with restored-input evaluation defined as an extension. RoboTactile establishes track-specific protocols for offline prediction and closed-loop manipulation across world models, world–action models, policies, and vision–language–action models. Experiments on four models reveal substantial task- and fault-dependent variation. We report absolute success, performance gaps, and descriptive tactile gain retention relative to available visual references, while distinguishing these from verified matched-control estimates. Matched visual controls, phase-conditioned evaluation, and post-restoration evaluation remain future work. We hope RoboTactile will support the development and evaluation of robust vision-tactile models.