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

arXiv: https://arxiv.org/abs/2002.01791 · HTML: https://mlsanity.com/paper/2002.01791/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2020.

## Built on by (most cited first, 4 of 4)

- [Visual-Tactile Sensing for Real-time Liquid Volume Estimation in Grasping](https://mlsanity.com/paper/2202.11503/) (2022)
- [Latent Diffeomorphic Co-Design of End-Effectors for Deformable and Fragile Object Manipulation](https://mlsanity.com/paper/2602.17921/) (2026)
- [IsoNet: Causal Analysis of Multimodal Transformers for Neuromuscular Gesture Classification](https://mlsanity.com/paper/2506.16744/) (2025)
- [VILAS: A VLA-Integrated Low-cost Architecture with Soft Grasping for Robotic Manipulation](https://mlsanity.com/paper/2605.02037/) (2026)

## Abstract (arXiv)

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.