BIFTA: Brain-Inspired Few-Shot Tactile Adaptation for Unknown Sensors

2026-09-08

AI Summary

为解决未知传感器上触觉模型性能下降问题，提出BIFTA框架，通过少量标记数据快速适应新传感器，显著提高跨传感器的触觉感知准确性。

Abstract

Advances in tactile sensing have made contact-rich perception possible, accelerating progress in robotic manipulation, material understanding, and embodied interaction. However, because optical design, elastomer mechanics, and imaging geometry differ substantially across tactile sensors, models trained on known sensor types can suffer an abrupt performance collapse on unknown sensors. To address this problem, we propose the Brain-Inspired Few-Shot Tactile Adaptation (BIFTA) framework; it draws on the brain's rapid sensory adaptation mechanism to adapt a frozen encoder to an unknown tactile sensor from a small labeled support set. BIFTA preserves pretrained representations through dual-view statistical memory, constructs support-conditioned spectral graphs to repair sensor-dependent feature neighborhoods, and applies uncertainty-gated recurrent propagation to strengthen reliable cross-query evidence. Extensive benchmarks across three tactile datasets show that BIFTA substantially improves adaptation to unknown sensors: with only 10\% labeled target data on SITR, it raises mean Sparsh accuracy from 6.86\% for the frozen source classifier to 87.09\%, exceeding the strongest implemented prior comparison by 47.22 percentage points, and these gains generalize across datasets, pretrained backbones, and tactile tasks. These results validate BIFTA for data-efficient adaptation to unknown tactile sensors and offer a promising route toward tactile models that transfer across heterogeneous hardware.

Problem

• tactile sensors
• performance collapse
• unknown sensors

Innovation

• Few-Shot Learning
• Tactile Sensing
• Sensory Adaptation
• Dual-View Statistical Memory
• Uncertainty-Gated Recurrent Propagation