Surformer v1: Transformer-Based Surface Classification Using Tactile and Vision Features

📄 arXiv: 2508.06566v1 📥 PDF

作者: Manish Kansana, Elias Hossain, Shahram Rahimi, Noorbakhsh Amiri Golilarz

分类: cs.CV, cs.AI

发布日期: 2025-08-07

💡 一句话要点

提出Surformer v1以解决表面材料识别问题

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 表面材料识别 多模态融合 触觉特征 视觉特征 Transformer架构 实时应用 机器人感知

📋 核心要点

  1. 现有的深度学习模型在视觉任务上取得了显著的性能，但在触觉与视觉的融合上仍存在挑战。
  2. Surformer v1通过结合结构化触觉特征和视觉嵌入，采用Transformer架构实现了高效的表面分类。
  3. 实验结果显示，Surformer v1在准确性和推理效率上均优于传统的多模态CNN，展现了其在实时应用中的优势。

📝 摘要（中文）

表面材料识别是机器人感知和物理交互中的关键组成部分，尤其是在利用触觉和视觉传感输入时。本研究提出了Surformer v1，这是一种基于Transformer的架构，旨在使用结构化触觉特征和通过ResNet-50提取的主成分分析（PCA）降维视觉嵌入进行表面分类。该模型集成了特定模态的编码器和跨模态注意力层，实现了视觉与触觉之间的丰富交互。实验结果表明，Surformer v1在准确性和推理时间上均表现优异，达到了99.4%的准确率和0.77毫秒的推理时间，展示了其在实时应用中的潜力。

🔬 方法详解

问题定义：本论文旨在解决表面材料识别中的模态融合问题，现有方法在处理触觉和视觉信息时存在效率低下和准确性不足的挑战。

核心思路：论文提出的Surformer v1架构通过结合结构化触觉特征和视觉信息，利用Transformer的跨模态注意力机制，增强了视觉与触觉之间的交互，从而提高分类性能。

技术框架：Surformer v1的整体架构包括触觉特征编码器、视觉特征编码器和跨模态注意力层，首先对触觉和视觉特征进行独立编码，然后通过注意力机制进行融合，最终输出分类结果。

关键创新：Surformer v1的主要创新在于其跨模态注意力机制的设计，使得触觉和视觉信息能够有效交互，显著提升了分类准确性和推理速度，相较于传统方法具有本质区别。

关键设计：在模型设计中，采用了结构化触觉特征和PCA降维的视觉嵌入，损失函数选择了适合多模态学习的交叉熵损失，网络结构上则使用了Transformer的编码器架构，以提高模型的学习能力和推理效率。

📊 实验亮点

实验结果显示，Surformer v1在表面材料识别任务中达到了99.4%的准确率，推理时间仅为0.77毫秒，显著优于其他对比模型，尤其是在多模态学习的效率和准确性方面表现突出。

🎯 应用场景

该研究的潜在应用领域包括机器人抓取、智能家居、自动驾驶等场景，能够显著提升机器人的环境感知能力和交互效率。未来，Surformer v1有望在更多实际应用中实现实时的表面材料识别，推动智能机器人技术的发展。

📄 摘要（原文）

Surface material recognition is a key component in robotic perception and physical interaction, particularly when leveraging both tactile and visual sensory inputs. In this work, we propose Surformer v1, a transformer-based architecture designed for surface classification using structured tactile features and PCA-reduced visual embeddings extracted via ResNet-50. The model integrates modality-specific encoders with cross-modal attention layers, enabling rich interactions between vision and touch. Currently, state-of-the-art deep learning models for vision tasks have achieved remarkable performance. With this in mind, our first set of experiments focused exclusively on tactile-only surface classification. Using feature engineering, we trained and evaluated multiple machine learning models, assessing their accuracy and inference time. We then implemented an encoder-only Transformer model tailored for tactile features. This model not only achieved the highest accuracy but also demonstrated significantly faster inference time compared to other evaluated models, highlighting its potential for real-time applications. To extend this investigation, we introduced a multimodal fusion setup by combining vision and tactile inputs. We trained both Surformer v1 (using structured features) and Multimodal CNN (using raw images) to examine the impact of feature-based versus image-based multimodal learning on classification accuracy and computational efficiency. The results showed that Surformer v1 achieved 99.4% accuracy with an inference time of 0.77 ms, while the Multimodal CNN achieved slightly higher accuracy but required significantly more inference time. These findings suggest Surformer v1 offers a compelling balance between accuracy, efficiency, and computational cost for surface material recognition.