Synchronized Online Friction Estimation and Adaptive Grasp Control for Robust Gentle Grasp

TL;DR

This paper presents a unified, real-time framework for gentle robotic grasping that combines friction estimation via particle filtering with adaptive control to enhance stability and robustness.

Contribution

It introduces a novel particle filter-based friction estimation method integrated with a reactive grasp controller operating synchronously in a closed-loop system.

Findings

Validated through extensive robotic experiments

Achieves stable and robust grasping under varying conditions

Demonstrates real-time, adaptive force modulation

Abstract

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.