Force-guided High-precision Grasping Control of Fragile and Deformable Objects using sEMG-based Force Prediction

TL;DR

This paper presents a non-invasive sEMG-based system that predicts gripping force with high accuracy to enable precise, force-guided control of robotic hands for handling fragile and deformable objects safely.

Contribution

It introduces a neural network regression model for force prediction from sEMG signals and integrates it into a force-guided control framework for delicate object manipulation.

Findings

Achieved high force prediction accuracy (R2 = 0.982).

Successfully grasped fragile objects without damage.

Demonstrated effective force control on deformable objects.

Abstract

Regulating contact forces with high precision is crucial for grasping and manipulating fragile or deformable objects. We aim to utilize the dexterity of human hands to regulate the contact forces for robotic hands and exploit human sensory-motor synergies in a wearable and non-invasive way. We extracted force information from the electric activities of skeletal muscles during their voluntary contractions through surface electromyography (sEMG). We built a regression model based on a Neural Network to predict the gripping force from the preprocessed sEMG signals and achieved high accuracy (R2 = 0.982). Based on the force command predicted from human muscles, we developed a force-guided control framework, where force control was realized via an admittance controller that tracked the predicted gripping force reference to grasp delicate and deformable objects. We demonstrated the effectiveness of the proposed method on a set of representative fragile and deformable objects from daily life, all of which were successfully grasped without any damage or deformation.