FeelAnyForce: 視覚ベース触覚センサの触感から接触力フィードバックを推定
FeelAnyForce: Estimating Contact Force Feedback from Tactile Sensation for Vision-Based Tactile Sensors
視覚ベース触覚センサを用いて任意物体への3D接触力（最大15N）を推定するマルチヘッドトランスフォーマを提案し、未見物体で平均絶対誤差4%を達成、他センサへのキャリブレーション手法も示した。
著者: Amir-Hossein Shahidzadeh, Gabriele Caddeo, Koushik Alapati, Lorenzo Natale, Cornelia Fermüller, Yiannis Aloimonos
分類: cs.RO, cs.CV
原文アブストラクト
In this paper, we tackle the problem of estimating 3D contact forces using vision-based tactile sensors. In particular, our goal is to estimate contact forces over a large range (up to 15 N) on any objects while generalizing across different vision-based tactile sensors. Thus, we collected a dataset of over 200K indentations using a robotic arm that pressed various indenters onto a GelSight Mini sensor mounted on a force sensor and then used the data to train a multi-head transformer for force regression. Strong generalization is achieved via accurate data collection and multi-objective optimization that leverages depth contact images. Despite being trained only on primitive shapes and textures, the regressor achieves a mean absolute error of 4\% on a dataset of unseen real-world objects. We further evaluate our approach's generalization capability to other GelSight mini and DIGIT sensors, and propose a reproducible calibration procedure for adapting the pre-trained model to other vision-based sensors. Furthermore, the method was evaluated on real-world tasks, including weighing objects and controlling the deformation of delicate objects, which relies on accurate force feedback. Project webpage: http://prg.cs.umd.edu/FeelAnyForce
