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

2026-02-02

AI Summary

This work addresses the challenge of simultaneously achieving stability and compliance in robotic grasping by proposing a novel synchronous closed-loop framework that tightly couples real-time contact friction perception with adaptive grasp control for the first time. The approach integrates visuo-tactile sensing, an online friction coefficient estimation algorithm based on particle filtering, and a reactive adaptive controller to establish a highly responsive perception-action loop. Experimental results demonstrate that the proposed framework significantly enhances both compliance and robustness across a variety of object grasping tasks, enabling stable maintenance of gentle yet reliable grasps.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Vision for Robotics & Autonomous Driving
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• friction estimation
• adaptive grasp control
• gentle grasp
• tactile sensing
• robotic manipulation

Innovation

• friction estimation
• adaptive grasp control
• vision-based tactile sensing
• particle filter
• closed-loop sensorimotor integration