有限要素解析に基づくGelSight Mini光学触覚センサの力分布推定学習
Learning Force Distribution Estimation for the GelSight Mini Optical Tactile Sensor Based on Finite Element Analysis
GelSight Miniの生画像からU-netで法線力・せん断力分布を直接予測する機械学習手法を提案し、有限要素解析で生成した教師データで訓練した。
著者: Erik Helmut, Luca Dziarski, Niklas Funk, Boris Belousov, Jan Peters
分類: cs.RO, cs.LG
原文アブストラクト
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 .
