光学式触覚センサの力校正に向けた深層ドメイン適応回帰
Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors
校正済みの光学式触覚センサから未校正のセンサへ力予測能力を転移する深層ドメイン適応手法を提案し、マーカーや照明、弾性体の違いを超えて高精度に3軸力を校正できることを示した。
著者: Zhuo Chen, Ni Ou, Jiaqi Jiang, Shan Luo
分類: cs.RO
原文アブストラクト
Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4\% in full force range) for normal force, and 0.095N (6.3\%) and 0.062N (4.1\%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors.
