We introduce a unified framework for gentle robotic grasping that synergistically couples real-time friction estimation with adaptive grasp control. We propose a new particle filter-based method for real-time estimation of the friction coefficient using vision-based tactile sensors. This estimate is seamlessly integrated into a reactive controller that dynamically modulates grasp force to maintain a stable grip. The two processes operate synchronously in a closed-loop: the controller uses the current best estimate to adjust the force, while new tactile feedback from this action continuously refines the estimation. This creates a highly responsive and robust sensorimotor cycle. The reliability and efficiency of the complete framework are validated through extensive robotic experiments.
@article{arxiv.2602.02026,
title = {Synchronized Online Friction Estimation and Adaptive Grasp Control for Robust Gentle Grasp},
author = {Zhenwei Niu and Xiaoyi Chen and Jiayu Hu and Zhaoyang Liu and Tang Jian and Xiaozu Ju},
journal= {arXiv preprint arXiv:2602.02026},
year = {2026}
}