  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.
