用深度学习从凝胶形变图像中精准估算力分布，提升机器人触觉感知能力。
Learning Force Distribution Estimation for the GelSight Mini Optical Tactile Sensor Based on Finite Element Analysis
- 基于有限元分析生成数据，用U-net直接从原始图像预测力分布。
- 对商用GelSight Mini传感器的法向与剪切力分布预测准确率高。
- 模型可泛化到不同压头、同类型传感器，适合实时触觉应用。
接触密集型操作仍是机器人领域的重大挑战。像GelSight Mini这样的光学触觉传感器通过捕捉硅胶软体的形变，提供低成本的接触感知方案。然而，如何从这些凝胶形变中准确推断出剪切力和法向力分布仍未完全解决。本文提出一种基于机器学习的方法，采用U-net架构，直接从传感器原始图像预测力分布。模型在由有限元分析（FEA）推导出的力分布数据上训练，展现出对商用GelSight Mini传感器法向力和剪切力分布的高精度预测能力。实验还表明该模型具备跨压头、同类型传感器间的泛化潜力，并支持实时应用。代码、数据集与模型已开源，详见 https://feats-ai.github.io。
原文摘要 · Abstract (English)
Contact-rich manipulation remains a major challenge in robotics. Optical tactile sensors like GelSight Mini offer a low-cost solution for contact sensing by capturing soft-body deformations of the silicone gel. However, accurately inferring shear and normal force distributions from these gel deformations has yet to be fully addressed. In this work, we propose a machine learning approach using a U-net architecture to predict force distributions directly from the sensor's raw images. Our model, trained on force distributions inferred from \ac{fea}, demonstrates promising accuracy in predicting normal and shear force distributions for the commercially available GelSight Mini sensor. It also shows potential for generalization across indenters, sensors of the same type, and for enabling real-time application. The codebase, dataset and models are open-sourced and available at https://feats-ai.github.io .
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