用视觉触觉传感器提升机器人对物体柔性的精准检测
Advances in Compliance Detection: Novel Models Using Vision-Based Tactile Sensors
- 基于LRCN和Transformer架构融合RGB触觉图像预测柔性
- 相比基线模型准确率显著提升，多指标验证有效
- 揭示传感器与物体硬度关系，硬物更难估计
柔度是工程、农业和生物医学应用中描述物体的关键参数。传统检测方法受限于便携性差、可扩展性低，依赖专用且昂贵设备，不适用于机器人场景。现有基于神经网络的视觉触觉传感器方法仍存在预测精度不足的问题。本文提出两种基于长时循环卷积网络（LRCN）和Transformer架构的模型，利用视觉触觉传感器GelSight捕捉的RGB触觉图像及其他信息，实现对柔度指标的高精度预测。通过多种评估指标验证，所提模型性能显著优于基线。此外，研究发现传感器柔度与物体柔度估计间存在相关性：当物体比传感器更硬时，估计难度增加。
原文摘要 · Abstract (English)
Compliance is a critical parameter for describing objects in engineering, agriculture, and biomedical applications. Traditional compliance detection methods are limited by their lack of portability and scalability, rely on specialized, often expensive equipment, and are unsuitable for robotic applications. Moreover, existing neural network-based approaches using vision-based tactile sensors still suffer from insufficient prediction accuracy. In this paper, we propose two models based on Long-term Recurrent Convolutional Networks (LRCNs) and Transformer architectures that leverage RGB tactile images and other information captured by the vision-based sensor GelSight to predict compliance metrics accurately. We validate the performance of these models using multiple metrics and demonstrate their effectiveness in accurately estimating compliance. The proposed models exhibit significant performance improvement over the baseline. Additionally, we investigated the correlation between sensor compliance and object compliance estimation, which revealed that objects that are harder than the sensor are more challenging to estimate.
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