Touch Has Memory: Learning Shared Response Models for Few-Shot Calibration of Soft Tactile Sensors

Maintaining accurate force estimation continuously after calibration is challenging for soft tactile sensors, whose responses vary across sensors and depend on loading history. For large-scale manufacturing, the challenge is to capture these history-dependent responses with expressive models while keeping local calibration effort low. We present a few-shot calibration framework that separates shared response modeling from local output calibration. We use four response models: static, path-dependent, temporal relaxation, and hysteresis. Each predicts normal contact force through a compact neural readout. We train the models independently on source recordings, using initial segments for calibration and later segments to optimize force prediction. At each new sensing location, we freeze the models and use a few initial force references to calibrate their output combination. After calibration, all model and calibration parameters remain fixed, while local memory states update causally from the incoming electrical data stream, enabling force estimation without further force references. We evaluate the framework at fourteen sensing locations across three tactile sensor arrays excluded from training, each containing 48 sensing elements. With three fitting references selected from dense initial force recordings, our framework achieves an overall RMSE of 0.375 N under full-window calibration, outperforming the evaluated external baselines. These results suggest that shared response models can support accurate force estimation under changing loading conditions while limiting local learning to output calibration.