A Model-based Visual Contact Localization and Force Sensing System for Compliant Robotic Grippers
Grasp force estimation can help prevent robots from damaging delicate objects during manipulation and improve learning-based robotic control. Integrating force sensing into deformable grippers negotiates trade-offs in cost, complexity, mechanical robustness, and performance. With the growing integration of RGB-D wrist cameras into robotic systems for control purposes, camera-based techniques are a promising solution for indirect visual force estimation. Current approaches mostly utilize end-to-end deep learning, which can be brittle when generalizing to new scenarios, while existing model-based approaches are unsuited to grasping and modern grasper geometries. To address these challenges,...
| Authors | Kaiwen Zuo, Shuyuan Yang, Zonghe Chua |
|---|---|
| Primary category | cs.RO |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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