An FBG tactile sensor array and self-supervised contrastive learning transformer for tumor depth estimation in robotic palpation

Robot-assisted minimally invasive surgery lacks haptic feedback for localizing subsurface tumors and estimating depth. We present a compact, high-density fiber Bragg grating (FBG) tactile sensor array for robotic palpation. A hexagonal close-packed topology increases spatial sampling within an 8.5-mm maximum lateral span while maintaining high sensitivity. Static and dynamic tests demonstrate linear force-wavelength responses across seven channels, a worst-case nominal interrogator-limited force resolution of 8.43 mN, and <1% full-scale dynamic error. We further introduce FBG-PatchFormer, which uses contrastive self-supervised pretraining on unlabeled palpation windows followed by supervised finetuning for depth classification. In phantoms with embedded inclusions, the model achieved 99.60% record-level accuracy for ten classes (blank and 2–10 mm) within the calibrated target-stiffness regime. Cross-condition tests showed transfer across inclusion shape and moderate surface curvature; performance under background-stiffness shift was partially recovered using three labeled records per class. In vivo porcine-liver experiments demonstrated force-map-based localization under physiological motion and fluid interference, supporting the feasibility of the sensing system for robotic palpation.