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Regulating contact forces with high precision is crucial for grasping andmanipulating fragile or deformable objects. We aim to utilize the dexterity ofhuman hands to regulate the contact forces for robotic hands and exploit humansensory-motor synergies in a wearable and non-invasive way. We extracted forceinformation from the electric activities of skeletal muscles during theirvoluntary contractions through surface electromyography (sEMG). We built aregression model based on a Neural Network to predict the gripping force fromthe preprocessed sEMG signals and achieved high accuracy (R2 = 0.982). Based onthe force command predicted from human muscles, we developed a force-guidedcontrol framework, where force control was realized via an admittancecontroller that tracked the predicted gripping force reference to graspdelicate and deformable objects. We demonstrated the effectiveness of theproposed method on a set of representative fragile and deformable objects fromdaily life, all of which were successfully grasped without any damage ordeformation.

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