BIFTA：面向未知传感器的脑启发式少样本触觉适应

中文摘要 AI 辅助

触觉传感的进步使得接触丰富的感知成为可能，加速了机器人操作、材料理解和具身交互的发展。然而，由于不同触觉传感器在光学设计、弹性体力学和成像几何上存在显著差异，在已知传感器类型上训练的模型在遇到未知传感器时可能会遭遇性能的急剧崩溃。为解决这一问题，我们提出了脑启发式少样本触觉适应（BIFTA）框架；它借鉴大脑的快速感觉适应机制，利用一个小型带标签的支持集，将冻结的编码器适应到未知的触觉传感器上。BIFTA通过双视角统计记忆保留预训练表示，构建支持条件谱图以修复传感器相关的特征邻域，并应用不确定性门控循环传播来增强可靠的跨查询证据。在三个触觉数据集上的广泛基准测试表明，BIFTA显著提高了对未知传感器的适应能力：在SITR上仅使用10%的带标签目标数据，它就将平均Sparsh准确率从冻结源分类器的6.86%提升至87.09%，超过最强已实现先前比较方法47.22个百分点，并且这些增益在数据集、预训练骨干网络和触觉任务中均能泛化。这些结果验证了BIFTA在数据高效适应未知触觉传感器方面的有效性，并为跨异构硬件迁移的触觉模型提供了一条有前景的途径。

英文摘要

Vision-based tactile sensors provide high-resolution contact information for robotic perception and contact-rich manipulation, advancing embodied intelligence through more reliable physical interaction. However, device-specific sensing mechanisms cause tactile foundation models to degrade on unfamiliar hardware. Existing cross-sensor methods often require calibration data, paired observations, or iterative training. To address this problem, we propose Brain-Inspired Data-Efficient Tactile Adaptation (BIDETA), a gradient-free framework that uses a frozen tactile encoder and a few labeled target contacts to jointly predict labels for an unlabeled query batch. Inspired by the brain's rapid sensory adaptation, BIDETA combines rapid support memory, support-conditioned spectral graphs, and reliability-gated recurrence to preserve pretrained representations, repair sensor-dependent feature neighborhoods, and integrate reliable cross-query evidence. Experiments on SITR, TacVerse Shape, and TacQuad show that BIDETA 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. In the SITR timing benchmark with TVL, BIDETA also achieves approximately 20x faster target-sensor adaptation than the best baseline. BIDETA thus offers a gradient-free, data-efficient route to deploying tactile models on new hardware.

发表机构

• School of Computer Science and Technology（计算机科学与技术学院）
• Beijing Institute of Technology（北京理工大学）

机构由 AI 辅助整理，请以论文原文为准。