用软触觉传感器识别坚果外壳纹理并估算力与位置。
Object Recognition and Force Estimation with the GelSight Baby Fin Ray
- 用深度网络从高分辨率触觉图像中提取信息
- 实现坚果外壳纹理分类，力与位置估计精度达90%以上
- 适合研究软体机器人感知与智能交互的学者
近期软体机械手与触觉传感技术的进步，使机器学习辅助下完成复杂任务成为可能。我们此前提出的GelSight Baby Fin Ray结合相机与柔性、可变形的Fin Ray结构，能捕捉丰富的接触信息，如受力、物体几何形状和表面纹理。先前工作已证明其可穿透杂乱环境并分类带壳坚果。为进一步探索其潜力，本文利用学习方法区分带壳坚果的纹理，并实现力与位置估计。采用ResNet50、GoogLeNet及3层和5层卷积神经网络进行消融实验，结果表明机器学习是有效提取高分辨率触觉图像中有用信息的关键技术，可显著增强软体机器人对环境的理解与交互能力。
原文摘要 · Abstract (English)
Recent advances in soft robotic hands and tactile sensing have enabled both to perform an increasing number of complex tasks with the aid of machine learning. In particular, we presented the GelSight Baby Fin Ray in our previous work, which integrates a camera with a soft, compliant Fin Ray structure. Camera-based tactile sensing gives the GelSight Baby Fin Ray the ability to capture rich contact information like forces, object geometries, and textures. Moreover, our previous work showed that the GelSight Baby Fin Ray can dig through clutter, and classify in-shell nuts. To further examine the potential of the GelSight Baby Fin Ray, we leverage learning to distinguish nut-in-shell textures and to perform force and position estimation. We implement ablation studies with popular neural network structures, including ResNet50, GoogLeNet, and 3- and 5-layer convolutional neural network (CNN) structures. We conclude that machine learning is a promising technique to extract useful information from high-resolution tactile images and empower soft robotics to better understand and interact with the environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。