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Force-Guided High-Precision Grasping Control of Fragile and Deformable Objects Using sEMG-Based Force Prediction

Research output: Contribution to journalArticlepeer-review

  • Ruoshi Wen
  • Kai Yuan
  • Qiang Wang
  • Shuai Heng
  • Zhibin Li

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Original languageEnglish
Pages (from-to)2762-2769
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
Early online date17 Feb 2020
Publication statusPublished - 30 Apr 2020


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.

    Research areas

  • Dexterous manipulation, grasping, force control

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