论文/arXiv 2609.08673·1 个月前

BIDETA：面向未知传感器的脑启发数据高效触觉适应

BIDETA用冻结触觉编码器和少量目标标注，无梯度适配未知触觉传感器，并在SITR等数据集验证。

AI 整理

• 硬件零部件
• 数据与采集
• 具身模型

原文链接：BIDETA: Brain-Inspired Data-Efficient Tactile Adaptation for Unseen Sensors（arXiv 2609.08673 · 发布日期：2026-09-08）

BIDETA: Brain-Inspired Data-Efficient Tactile Adaptation for Unseen Sensors

BIDETA用冻结触觉编码器和少量目标标注，无梯度适配未知触觉传感器，并在SITR等数据集验证。

核心要点

• 视觉触觉传感器因设备机制差异，使触觉基础模型在陌生硬件上性能下降。
• BIDETA冻结触觉编码器，用少量标注目标接触与未标注查询批联合预测标签。
• 其结合快速支持记忆、支持条件谱图和可靠性门控递归，以保持并修复特征邻域。
• 实验显示其在多个数据集、骨干和触觉任务上提升适应，SITR计时基准约快20倍。

验证范围与局限：验证范围为SITR、TacVerse Shape、TacQuad及SITR计时基准上的未知触觉传感器适应。

英文摘要 (Abstract)

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.

作者：Bo-Heng Liu, Lan Wei, Ziyu Li, Chenghua Duan, Qing Li, Dandan Zhang, Xia Wu

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2609.08673：BIDETA: Brain-Inspired Data-Efficient Tactile Adaptation for Unseen Sensors访问 2026-10-21